F Rousseau

O Glenn

C Rodriguez-Carranza

D Vigneron

J Barkovich

C Studholme

Registration-based Approach for Reconstruction of High Resolution In Utero Fetal MR Brain Images

Keywords: Fetal MRI, Registration, 3D Reconstruction, High Resolution a : LSIIT

This paper describes a novel approach to forming high resolution MR images of the human fetal brain. It addresses the key problem of fetal motion by proposing a registration refined compounding of multiple sets of orthogonal fast 2D MRI slices, that are currently acquired for clinical studies, into a single high resolution MRI volume.

A robust multi-resolution slice alignment is applied iteratively to the data to correct motion of the fetus that occurs between 2D acquisitions. This is combined with an intensity correction step and a super resolution reconstruction step, to form a single high isotropic resolution volume of the fetal brain.

Experimental validation on synthetic image data with known motion types and underlying anatomy, together with retrospective application to sets of clinical acquisitions, are included.

Results indicate that this method promises a unique route to acquiring high resolution MRI of the fetal brain in vivo allowing comparable quality to that of neonatal MRI. Such data provides a highly valuable window into the process of normal and abnormal brain development which is directly applicable in a clinical setting.

Introduction

Ultrasound is the imaging modality of choice for the screening evaluation of the developing fetal brain. However, once an abnormality is suspected by routine prenatal ultrasound, further evaluation with MRI is indicated [START_REF] Levine | Fetal magnetic resonance imaging[END_REF][START_REF] Coakley | Fetal mr imaging: A developing modality for the developing patient[END_REF]. Indeed, fetal MRI can detect additional abnormalities not detectable by prenatal ultrasound, including gyral and sulcal abnormalities [START_REF] Coakley | Fetal mr imaging: A developing modality for the developing patient[END_REF][START_REF] Sonigo | Mr imaging of fetal cerebral anomalies[END_REF][START_REF] Garel | Fetal cerebral cortex: normal gestational landmarks identified using prenatal mr imaging[END_REF][START_REF] Girard | Fetal brain mr imaging[END_REF][START_REF] Glenn | Fetal central nervous system mr imaging[END_REF].

MRI allows for excellent tissue contrast and spatial resolution, uses no ionizing radiation, permits imaging in multiple planes, has a large field of view and allows for visualization of fetal anatomy in a manner not possible with ultrasound. The development of ultrafast 2D acquisition sequences has led to significant improvements in the clinical utility of fetal MRI [START_REF] Huppi | Magnetic resonance techniques in the evaluation of the perinatal brain: recent advances and future directions[END_REF][START_REF] Prayer | Fetal mri: techniques and protocols[END_REF]. However, the slice acquisition time is still very critical and has to be as short as possible to reduce the impact of fetal and maternal motion on the exam and for the comfort of the mother, since fetal MRI is often performed without sedation. As a result, sets of thick 2D slices are generally acquired in clinical studies, with motion commonly occurring between slices. Overall, the resulting image data is limited in its geometric integrity between slices due to motion, and in its through plane spatial resolution. In addition, body surface coils used in the imaging processes do not provide a homogeneous sensitivity over the entire field of view and, combined with motion during the acquisition, can then produce changes over time in the distortion profile for the same anatomical region (see figure3). This inhomogeneity is due to the fact that the gradients are linear over central region, but then fall off along the length of the scanner. There are also regions at both ends where the gradient strength is the same as in the center.

This work is motivated by the observation that current clinical imaging protocols make use of multiple orthogonal 2D multi-planar acquisitions to study the fetal brain. However, because of the motion between the slices, current techniques do not allow direct quantitative measurements to be made on the 3D anatomy. The aim of this work is to develop and apply registration based reconstruction methods to the problem of correcting motion and intensity distortions between the different 2D acquisitions, and to then reconstruct a single higher resolution MR image of the fetal brain.

Super Resolution Methods

The principle of super-resolution is to combine low resolution images to produce an image that has a higher spatial resolution than the original images [START_REF] Borman | Topics in Multiframe Superresolution Restoration[END_REF]. This is a large research field encompassing many applications, however the majority of the work has focused on using lower resolution data acquired on a regular grid and often assuming simple translational motion between the lower resolution sample grids, unlike our data which is corrupted by full 3D rigid motion on a slice by slice basis.

Overall published methods can be categorized into two main divisions: frequency domain and spatial domain. Frequency domain methods are based on Fourier transform properties. Denote a continuous 2D image f (x, y) and n shifted images f r (x + δx r , y + δy r ). Let the set {f r (i, j)}, with i ∈ {1...M}, j ∈ {1...N} and r ∈ {1...n} be the discrete version of the set {f r (x, y)} using an ideal uniform sampling with periods T x and T y . The continuous Fourier transform (CFT) F r of the continuous image and the discrete Fourier transform (DFT) Y r of the shifted and discrete images are related via aliasing:

Y r (m, n) = 1 T x T y +∞ i=-∞ +∞ j=-∞ F r 2π T x m M + i , 2π T y n N + j . (1)
Assuming that f (x, y) is band limited and using the shifting property of the Fourier transform, the equation ( 1) can be rewritten in matrix form as

Y = ΦF ( 2
)
where Y is a vector containing the DFT coefficients Y r (m, n) and F is a vector containing the unknown CFT of f (x, y). The super resolution restoration requires finding the DFT's of the observed images, determining the motion matrix Φ, solving the system of equations (2) for F and applying the inverse DFT to compute the reconstructed image.

The key advantages of frequency domain methods are their theoretical simplicity and the low computational complexity. Their disadvantages include the limitation to global translational motion, a spatially invariant degradation model and limited ability to vary the point spread function over the image.

In spatial domain methods, the most common approach is to model the physical problem and to invert it. These approaches use a generic observation model of the form:

y r = DBW r z + n r f or 1 ≤ r ≤ n (3)
where

• y r , r ∈ {1...n} denotes the low resolution images,

• z is the high resolution image,

• n r represents observation noise,

• D is the subsampling matrix,

• B a blur matrix,

• W r is the geometric transformation of rth low resolution image.

The three operators can be combined into a single matrix H r : H r = DBW r . The matrix H r thus incorporates motion compensation, degradation effects, and sub-sampling.

The problem is usually decomposed into two parts: motion estimation (estimating the matrix H r for each y r ) and super-resolution reconstruction (estimating z using the observation model). Accurate motion estimation which is a critical aspect of super-resolution methods, can be performed using block matching techniques or optical flow methods for instance. Please see [START_REF] Mitiche | Computation and analysis of image motion : a synopsis of current problems and methods[END_REF][START_REF] Stiller | Estimating motion in image sequences[END_REF] for further details about motion estimation. The super-resolution reconstruction can be performed using Simulate and Correct methods [START_REF] Peleg | Improving image resolution using subpixel motion[END_REF][START_REF] Irani | Improving resolution by image registration[END_REF], Projection Onto Convex Sets methods [START_REF] Patti | Superresolution video reconstruction with arbitrary sampling lattices and nonzero aperture time[END_REF] and Bayesian methods [START_REF] Schultz | Extraction of high resolution frames from video sequences[END_REF][START_REF] Hardie | Joint map registration and high-resolution image estimation using a sequence of undersampled images[END_REF]. One can note that in [START_REF] Hardie | Joint map registration and high-resolution image estimation using a sequence of undersampled images[END_REF], motion estimation and the high resolution image are jointly estimated. These spatial domain methods offer important advantages in terms of flexibility but they can be computationally very expensive. Lastly, another spatial domain approach is that of Interpolation-Restoration [START_REF] Tekalp | High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration[END_REF]. In this case a superresolution image is reconstructed from low resolution images using techniques for reconstruction from non-uniformly spaced samples, and restoration techniques can be applied to compensate for degradations. Such a general approach is suited to our problem of slice-by-slice motion corruption, and we will take this direction in our work.

Application to MRI

Building a high resolution 3D MR image of the fetal brain is challenging because of the original 2D slice thickness, the intensity distortion, and the unpredictable fetal motion which can occur in any direction and can be a much as several centimeters in distance between slice acquisitions. As far as we know, this problem which has been discussed before in the literature [START_REF] Schierlitz | Three-dimensional magnetic resonance imaging of fetal brains[END_REF][START_REF] Cachia | A primal sketch of the cortex mean curvature: a morphogenesis based approach to study the variability of the folding patterns[END_REF] is still an open issue. Super-resolution has previously been investigated in MRI using a specialized protocol to acquire shifted images [START_REF] Peeters | The use of super-resolution techniques to reduce slice thickness in functional mri[END_REF][START_REF] Greenspan | Mri inter-slice reconstruction using super-resolution[END_REF], where the motion between slices is a known translation in the slice direction. Moore et al. in [START_REF] Moore | A high resolution dynamic heart model based on averaged mri data[END_REF] built a high resolution dynamic heart model from isotropic coronal slices. The misalignment was corrected by registering a volume with sagittal and axial scout images. Image intensities were averaged together to generate a high resolution volume. Lotjonen et al. in [START_REF] Lotjonen | Correction of movement artifacts from 4-d cardiac short-and long-axis mr data[END_REF] have proposed an algorithm to correct translation-only movement artifacts simultaneously from short-and long-axis cardiac MR series. Locations of short-axis slices were optimized based on data from long-axis slices and vice versa. No reconstruction of a higher resolution volume of the heart was done.

The task of compounding multiple orthogonal sets of low resolution fetal MRI slices into a single high resolution dataset shares a number of common features with the published methods in the super-resolution literature, but presents significant additional problems. Specifically, we have multiple orthogonal datasets that have non-isotropic voxel size, we have subject motion during slice acquisitions so that each 2D slice is misaligned with respect to others in the acquisition, and the resulting stack of low resolution 2D slices does not have a consistent 3D geometry. This movement can also mean a loss of spatial information between slices, in regions of anatomy missed in the acquisition due to the motion. The fully 3D motion artifacts make a manual motion correction procedure unfeasible. Moreover, because the intensity inhomogeneity of the MRI acquisition, the movement of the fetus within the field of view can induce large scale changes in the illumination pattern between acquisitions.

In this article we report our work on this challenging problem and describe a fully automatic and accurate algorithm to correct slice misalignments, correct relative intensity distortions, and reconstruct a single high resolution image from sets of clinically typical low resolution fetal MRI data.

Method

Our overall aim is to reconstruct an image of the fetal brain that has increased and isotropic sampling resolution, increased overall spatial resolution (reduced point spread function), and consistent tissue intensity over the field of view. The resolution of the source data is typically 1x1mm in plane with 3mm thick slices. Multiple sets consisting of between 20 and 40 slices each are acquired over a period of around 20 seconds for each set. The slices are commonly acquired in an interleaved pattern (usually alternating odd-even) and significant movement of a centimeter or more can occur during the acquisition of one set of slices. If the fetus moves quickly at the wrong time, single slices can be corrupted due to motion. This is relatively rare, but typically in the clinical protocol, these images are visually detected and the acquisition repeated, until at least two nonmotion degraded sets of axial, sagittal, and coronal slices of the fetal brain are obtained. It is therefore reasonable to assume (and has be confirmed visually) in this type of data, that motion corrupted slices are rare within the data we have to reconstruct. However the algorithm could be applied with slices removed where within slice motion occurs, by making use of the other orthogonal datasets to fill in the missing structure.

We posed the task of recovering the local relative alignment of each slice in 3 dimensions, but can make use of the consistent geometry provided in the through plane direction of a slice that is provided by the other orthogonal sets of slice data, to constrain the collective alignment of the multiple low resolution images.

The proposed method is made up of three steps: motion correction, volume reconstruction and contrast correction.

Notations

Let us consider n low resolution 3D images, denoted by I i LR , i ∈ {1...n}. A so-called low resolution 3D image is a stack of 2D thick slices. Such a notation is used in order to keep in mind that the low resolution source data are not geometrically consistent. The reconstructed high resolution volume is denoted by I HR .

Motion Correction

In order to correct fetal motion, we propose to use a registration based method. Any registration method consists of three components [START_REF] Maintz | A survey of medical image registration[END_REF]: a similarity measure between the two images, a transformation relating the coordinate system of one image to that of another image, and an optimization procedure to improve the transformation between the two images with respect to the similarity measure.

Registration methods are usually classified as being either sparse (feature) or dense (voxel) based. In the case of fetal brain MRI registration, because of the 3D non-consistency of the scans, finding homologous 3D landmarks is a very challenging task and we have therefore approached the problem using a voxel based method. Registration methods rely on the fact that the similarity measure is optimal when the two images are perfectly aligned. The choice of one measure relies on the assumptions relating intensities in the two images: an identity relationship, an affine relationship, a functional relationship and a statistical relationship. Due to the motion and contrast artifacts corrupting fetal MRI, it is highly desirable to use a similarity measure that is not based on strong assumptions between the two images. Mutual information is a measure of the statistical dependence between two random variables [START_REF] Collignon | Automated multimodality image registration using information theory[END_REF][START_REF] Viola | Alignment by maximisation of mutual information[END_REF]. The mutual information MI(X, Y ) is defined as follows:

MI(X, Y ) = h(X) + h(Y ) -h(X, Y ) (4)
where the marginal entropy h(X) is h(X) =f X (x) log f X (x)dx and where the joint entropy

h(X, Y ) is h(X, Y ) = -f XY (x, y) log f XY (x, y)dxdy.
To reduce the sensitivity to changes in overlap, we use the normalized mutual information [START_REF] Studholme | An overlap invariant entropy measure of 3d medical image alignment[END_REF] which is:

NMI(X, Y ) = h(X) + h(Y ) h(X, Y ) . (5)
Maximization of NMI(X, Y ) seeks a transformation where joint entropy is minimized with respect to the marginal entropies. Motion correction is performed by aligning globally the low resolution images together and then by aligning every slice of the low resolution images to the reconstructed high resolution volume.

Low Resolution Image Registration

The first step consists of approximately globally aligning the n low resolution images. One low resolution image is chosen arbitrarily as the reference image and is used to define a global coordinate system. The other low resolution images are rigidly registered to the reference image (6 degrees of freedom: 3 translations and 3 rotations). The 3D rigid transformation between an image I i LR and the reference image is denoted T i 1 . The high resolution volume coordinate system is chosen to match with the global coordinate system defined previously. Figure 1 illustrates the two transformations for each slice and the different coordinate systems used during the slice and image registration.

Slice Registration

Once the low resolution images are roughly globally aligned, slice motion artifacts are corrected. The low resolution image acquisition is interleaved so that odd and then even slices are acquired in sequence. As the fetus can move during the exam and the observed structures are small, consecutive slices may present completely different anatomical structures. Therefore, we cannot register the slices using, for instance, a correlation assumption between consecutive slices. Slice motion is therefore corrected by rigidly registering a slice to the current reconstructed high resolution volume. This high resolution volume is built using the current estimate of slice positions.

We register the slices of low resolution image I i LR with the volume reconstructed using the n -1 other low resolution images I j LR , j ∈ {1...n, j = i}. A local coordinate system is defined for every slice and the transformation between a slice and the high resolution volume is composed from the two rigid transformations:

y i k = T i 1 T i 2 k x i k (6)
where T i 2 k denotes the 3D rigid transformation (6 degrees of freedom) from the slice k of the low resolution image i to low resolution image i coordinates, x i k is the slice pixel coordinates and y i k the voxel coordinates in the compounded high resolution volume (see Figure 1).

Optimization

For both image and slice registration, a gradient ascent method is used to maximize the normalized mutual information. Concerning slice registration, we exploit the interleaved acquisition to propose an hierarchical approach to provide a starting estimate to the optimization procedure. Slices of a low resolution image are ordered by acquisition time and are separated into two groups. The optimization consists in finding the optimal rigid transformations T 2 between the new groups of slices and the low resolution image coordinate system. The similarity measure is computed between the current reconstructed volume and the group of slices. This is performed for the n low resolution images. Once the convergence criterion is reached, each group is divided into two groups and the optimization procedure is then performed on these new groups of slices (see Figure 2). For the final phase of alignment, we maximize independently the normalized mutual information between each slice and the current reconstructed high resolution volume. This uses an iterative slice-by-slice scheme, looping over all slices until the sum of the slice-to-volume criteria (called the global convergence criterion) does not increase. For each 2D-3D registration step, a two-level multiresolution strategy is applied to avoid the pitfall of local optima.

Volume Reconstruction

The reconstruction problem can be classified as scattered interpolation [START_REF] Mitas | Spatial interpolation[END_REF]. As a volume reconstruction is required repeatedly during the registration and contrast correction steps, the reconstruction algorithm must be computationally efficient. To deal with the computation time constraint, a local neighborhood approach has been adopted. Local methods are based on the assumption that each point influences the resulting volume only up to a certain finite distance.

For each pixel x in the slices of the original low resolution scans, the 3D coordinates y of x in the high resolution volume are calculated taking into account the results of the image and slice registration. The intensity value of x is injected into the reconstructed volume using a kernel f centered at y. If the kernel is a narrow boxcar function, the reconstruction method approximates a the pixel nearest neighbor (PNN) method [START_REF] Rohling | A comparison of freehand three-dimensional ultrasound reconstruction techniques[END_REF]. If the kernel is a pyramid function, the method is referred to pixel trilinear (PTL) interpolation [START_REF] Gobbi | Brain deformation correction using interactive 3D ultrasound imaging[END_REF]. Our choice of kernel is motivated by need to match the underlying point spread function of the data. In the through slice direction this can be modeled as a Gaussian [START_REF] Ohbuchi | Incremental volume reconstruction and rendering for 3d ultrasound imaging[END_REF]. The Gaussian kernel is perpendicular to the slice plane, and the standard deviation determining the profile is chosen to match the known acquisition profile. Within a slice plane of the MRI, the point spread function can often be modeled as a sinc, but for simplicity we have used a narrow Gaussian. Local methods such as these have the advantage of making it possible to update the compounded image incrementally within a limited spatial extent, thus requiring lower computational burden.

Using local reconstruction methods with a truncated kernel for speed, gaps can occur in the reconstructed volume when sudden significant motion occurs, and there is no data contributing to a given reconstructed point. Empty voxels are filled using the average of the surrounding voxels. This local approach has the advantage that it can be implemented very efficiently. In practice, using a oriented Gaussian kernel, we found that gaps do not occur using three or more orthogonal low resolution images.

Contrast Correction

We employ a contrast correction step to correct the local relative intensity distortion between the low resolution images to allow accurate compounding. Intensity changes from one 2D slice to another are both global and local. To address this issue, one low resolution image is used as a reference for tissue intensities and the other low resolution images are corrected to it. We consider the relationship between contrasts in the low resolution image to be corrected and the reference image as a spatially varying multiplicative field which is assumed to vary smoothly over the field of view. A direct approach to estimation is used where the two volumes are low pass filtered with a Gaussian kernel G. The relative scaling in intensity β i (x) at location x of the low resolution image I i LR (x) is estimated for all slice pixels x as follows:

β i (x) = a i * G(IR(x)) G(I i LR (x))
with a i = x I i LR (x)

x I i LR (x) G(IR(x)) G(I i LR (x)) (7)
where IR is the intensity reference image, I i LR is the low resolution image considered and a i is used to keep the mean value of the image I i LR unchanged. IR is an arbitrary low resolution image projected in the reconstruction space. The corrected low resolution image Îi LR is then:

Îi LR (x) = β i (x)I i LR (x).

Overview of the algorithm

The proposed method consists in the following steps:

1. Apply global rigid registration between all the low resolution images. Where the slice registration algorithm is the following:

Create initial slice registration estimate by hierarchical application of Slice Registration Algorithm

1. For each low resolution image I i LR :

(a) Reconstruct a high resolution volume using the low resolution images I j LR , j ∈ {1...n, j = i}, (b) For each slice in I i LR , perform the 2D-3D registration.

3 Material and Evaluation Procedure

Simulated Motions on Real MR Datasets

Initially we want to explore the ability to recover slice to volume alignment for typical but known motion, on typical anatomical structures. In order to perform the most realistic simulation possible, we used as a starting point 3D MRI acquisitions of four premature neonates with high quality and no motion. The post conceptional age of the premature neonates at the scan acquisition was 28, 32.6, 34.3 and 42.6 weeks. T1 weighted SPGR images were acquired on a 1.5T GE MRI scanner with a dedicated MRI compatible incubator and had a spatial resolution of 0.7 x 0.7 x 2.1mm. The 3D image was interpolated to isotropic voxels using B-spline interpolation. Figure 4 shows coronal views of the four premature neonates.

In order to evaluate the slice registration algorithm, anisotropic subsampled volumes with 6 degrees of freedom (DOF) motion artifact were simulated from the isotropic neonatal image. Motion artifacts were defined by the maximum magnitude of the head motion and by the head motion type. The first simulated motion was a sudden motion. It simulated a short fast movement of the head during a short portion of the acquisition. The model used for the simulation incorporates the interleaved aspect of acquisition. The second type of motion was a temporally smooth motion simulated using B-Splines. In both simulations, some information is lost due to missed portions of the underlying anatomy. The displacements were chosen from a uniform distribution with a varying range for [-x, x]mm translations in each direction and between [-x, x] degrees for each rotation, x ∈ {1, 5, 10}. Compared with the size of fetal brain, a head motion of ±10mm in translation is a significant fraction of the diameter of the brain.

Figure 5 shows one isotropic premature neonate data and two examples of simulated low resolution images using random motions (sudden motion and smooth motion with a maximum magnitude of 10mm for the translation and 10 degrees for the rotations). These simulations from a known underlying anatomy allow us to assess both the accuracy and the robustness of the slice registration process. Considering n simulated low resolution images already globally aligned, the hierarchical and the multiresolution strategies are applied for the slice registration. Evaluation of the algorithm has been done using 24 simulations.

The accuracy was assessed by computing a registration error measured on a set of 4 points P i distributed within every slices as follows: RMS =

1 n n i=1 T RE 2 i
, where TRE is the target registration error defined as follows: T RE i = P i -T -1 (T * (P i )) 2 . T * denotes the known applied motion transformation and T is the estimated geometric transformation. P i form a square of 100mm by 100mm in which the fetal brain is included. The error previously defined provides thus a maximum bound of the registration error for the region of interest.

Fetal Datasets

A typical clinical study of a fetus at our institute follows the following procedure: a quick localizer sequence is obtained in less than 30 seconds during maternal free breathing in order to determine the location of the fetal head. No sedation or contrast agents were administered during the study, and this study was approved by the UCSF IRB. Conventional Single-shot Fast Spin-echo (SSFSE) T2-weighted images are then acquired during normal maternal breathing (a single slice is acquired in less than one second). Sets of contiguous slices were acquired in the axial, sagittal, and coronal planes with respect to the fetal brain, based on the initial localizer. All images were acquired in an interleaved manner to reduce saturation of spins in adjacent slices due to fetal motion during the sequence. The sequence parameters were: TR=6000, TE effective = 90 ms, field of view = 240mm (FOV), in plane resolution about 1mm, 3mm slice thickness. The reconstruction method has been performed on a database of 17 subjects.

Results

From the simulated motion experiments on premature neonatal data, we evaluated the RMS registration error for four points at the corners of a box within the brain tissue of size 100mm× 100mm for each slice. These are presented in Figure 6. The simulated starting RMS error is up to 12mm. Each point represents the result of one simulation and its coordinates are the starting RMS error and the final RMS error. For all cases the final overall slice alignment error was significantly reduced by the alignment process. Overall, even with the presence of strong motion artifacts and significantly varying developmental age, RMS errors remained below 1mm. In all the cases, visual inspection of the obtained results was also very satisfactory (see Figure 5). These experiments show the robustness of the proposed method to significant fetal head motion and demonstrate that the stage of development of the underlying brain anatomy does not affect the registration accuracy.

We then applied the algorithm to clinically acquired fetal MRI datasets using the same optimization parameters. Each dataset consists of three orthogonal low resolution images (axial, coronal, sagittal). Figure 3 shows the high resolution reconstructed volume compared to the three original low resolution images. It clearly shows the quality improvement in terms of resolution and motion correction provided by the proposed compounding method, even with the presence of significant motion artifacts.

Figure 7 shows one low resolution image and the high resolution reconstructed image, and the corresponding segmented brain from the original coronal low resolution and the high resolution. The surface rendering of semi-interactive brain surface segmentation has been performed using the rview software1 . This figure clearly illustrates the importance of a geometrically consistent and intensity consistent image when interpreting cortical development. In the compounded image, early stages of the formation of the temporal and frontal gyri are seen. In addition, in the original data one can note that when the fetal brain is not entirely scanned in one of the sets, but compounding multiple image sets where the extremities were scanned, allows recovery of the whole brain.

Finally, Figure 8 shows the results of the proposed method at three different time points for the same subject (25, 30 and 34 weeks). Overall, the results in terms of contrast and resolution are visually very satisfactory, whatever the age of the subject. Such quality of the reconstructed images allows us to study the early stages of the formation of the cortex.

Discussion

The ability to study the developing fetal brain in high resolution promises to provide a vital source of clinical information which could contribute directly to a number of challenging clinical questions. It will permit the use of many quantitative morphometric analysis methods, originally developed to study the adult [START_REF] Studholme | A Deformation Tensor Morphometry Study of Semantic Dementia with Quantitative Validation[END_REF] and neonatal brain [START_REF] Studholme | A Deformation Morphometry study of the Influences on the Pattern of Brain Tissue Development in Premature Neonates. Proceedings of Human Brain Mapping[END_REF][START_REF] Rodriguez-Carranza | An iso-surface folding analysis method applied to premature neonatal brain development[END_REF], to be applied to examine the process of in utero brain development. Critically, high resolution imaging is the key to seeing the process of cortical folding (in utero [START_REF] Inder | Periventricular white matter injury in the premature infant is followed by reduced cerebral cortical gray matter volume at term[END_REF][START_REF] Cachia | A primal sketch of the cortex mean curvature: a morphogenesis based approach to study the variability of the folding patterns[END_REF] and post mortem [START_REF] Batchelor | Measures of folding applied to the development of the human fetal brain[END_REF]), while consistent contrast allows us to study patterns of myelination. Such data is valuable both to specific clinical questions and, more fundamentally, to an improved understanding of the process of human brain development.

In this paper we have described a new methodology to reconstruct in utero high resolution 3D MRI scans of the fetal brain by proposing to compound multiple sets of orthogonal 2D MRI slices. The method makes use of a novel combination of 2D to 3D registration, relative inhomogeneity correction and high resolution reconstruction from sparse data. This is achieved by first globally registering the low resolution images, and then applying an iterative slice alignment scheme which seeks to refine the 3D positioning of each slice to the current combined high resolution volume. This step is embedded in a spatio-temporal acquisition-ordered hierarchical scheme to recover the fetal brain trajectory, which employs multiple image resolution refinement at each step. This is driven by normalized mutual information to provide robustness to contrast variation induced my motion of the fetal brain with respect to the imaging coil in the magnet. As a final step, a relative intensity correction is applied between the low resolution images to remove the differences in relative signal strength across the different acquisitions in each region of the fetal brain.

In the proposed approach, an arbitrary low resolution image is used as a reference image for the registration step and for the intensity correction method. Since this reference is only used for the estimation of the initial global rigid transformation, the choice of image does not have a significant impact on the final registration result. The choice of the reference image for the intensity correction step is also not critical, since the purpose of this part of the algorithm is simply to correct the relative contrast variations between the acquisitions for compounding. An absolute intensity correction can then be applied after reconstruction. One assumption we have made is that fetal motion within the time period of one slice is negligible. Since this assumption is reasonable in most of the cases, it may happen that one slice is obscured by motion artifact during the time of imaging. In such case, the corrupted slice can be manually removed from the stack of slices. Further work concerning this aspect would be to remove automatically corrupted slices using intensity information between consecutive slices.

We have used a kernel-based local approach for the reconstruction step of the algorithm. This approach can handle data with anisotropic resolution and is computationally efficient. The overall algorithm thus takes less than 20 minutes to register three low resolution images and to reconstruct a high resolution volume, running on a 3GHz Pentium 4 CPU. Although the proposed method allows us to successfully recover the 3D geometry of the fetal brain, there is still a slight loss of contrast in the reconstructed images. The reconstruction issue can be seen as an approximation problem with sparse data and anisotropic points. A possible way to improve the final quality of the reconstructed volume could be to develop a dedicated approximation method fast enough to handle these specific data. Moreover, further work is needed to evaluate the influence of the different sources of errors on the reconstructed volume quality: registration errors, intensity contrast variations and the reconstruction method. An elegant manner to reconstruct high resolution fetal MR volume would be to include all these sources of errors in an unified framework.

Visual inspection of the obtained results on clinical data are very encouraging. The developed algorithm automatically reconstructs a 3D high resolution geometrically consistent volume and it has proved to be robust to large artifacts. This represents an important step towards 3D quantitative analysis of the fetal brain. Further work is needed to better understand the capabilities and limitations of the approach on a range of fetal motion profiles and image quality. Moreover, in the future, we would like to investigate the use of the proposed method to study other fetal organs and also for other modalities such as functional MRI or diffusion tensor imaging (DTI) when images are motion corrupted.

Acknowledgment This work was supported by a Whitaker foundation award RG-01-0115, a NIH Grant R01-MH65392 and a NIH Biomedical Research Partnership grant, R01-EB0822. High resolution reconstructed images at three different time points for the same subject (25, 30 and 34 weeks). This result illustrates that whatever the age of the subject, the quality of the reconstruction volumes is visually very satisfactory. 21 (25, 30 and 34 weeks). This result illustrates that whatever the age of the subject, the quality of the reconstruction volumes is visually very satisfactory.

List of Figures

relative intensity distortion correction 5 .

 5 Reconstruct the final high resolution volume

Figure 1 :

 1 Figure 1: Coordinate systems used during the slice and image registration. Two rigid transformations are considered for each slice: T i 2 k between the slice k and the low resolution image i and T i 1 between the low resolution image i and the high resolution volume.

Figure 2 :

 2 Figure 2: Optimization example for slice registration exploiting interleaved acquisition. The slices are grouped by time (in this example an image of 8 slices is considered). At the first step, the optimization is performed for two groups of 4 slices each. At the next step, each group is divided into two groups. At the end, the optimization is performed for each slice independently.

Figure 3 :Figure 4 :Figure 5 :

 345 Figure 3: Acquired fetal MR image data (top 3 rows) with a resolution of 1x1x3mm. It can be noted that strong motion artefact corrupt the coronal acquisition (second row). Lower row shows the resulting reconstructed high resolution image (resolution 1x1x1mm).

Figure 6 :

 6 Figure6: Simulation results for premature neonate MR images: starting RMS errors versus final RMS errors in mm using two different simulated motions (sudden motion and smooth motion). Each of the 24 simulations is represented by a point. For all cases, final RMS errors remained below 1mm (please note the difference of scaling between starting RMS and final RMS).

Figure 7 :

 7 Figure 7: High Resolution fetal MR image (2nd row) reconstructed from low resolution images (1st row, coronal acquisition). 3rd row: Surface rendering of semi-automatic brain surface segmentation performed on original low resolution image and on high resolution reconstructed image.

Figure 8 :

 8 Figure 8: High resolution reconstructed images at three different time points for the same subject (25, 30 and 34 weeks). This result illustrates that whatever the age of the subject, the quality of the reconstruction volumes is visually very satisfactory.

1

  Coordinate systems used during the slice and image registration. Two rigid transformations are considered for each slice: T i 2 k between the slice k and the low resolution image i and T i 1 between the low resolution image i and the high resolution volume. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2 Optimization example for slice registration exploiting interleaved acquisition. The slices are grouped by time (in this example an image of 8 slices is considered). At the first step, the optimization is performed for two groups of 4 slices each. At the next step, each group is divided into two groups. At the end, the optimization is performed for each slice independently. . . . . . . . . . . . . . 15 3 Acquired fetal MR image data (top 3 rows) with a resolution of 1x1x3mm. It can be noted that strong motion artefact corrupt the coronal acquisition (second row). Lower row shows the resulting reconstructed high resolution image (resolution 1x1x1mm). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 4 Isotropic high resolution premature neonatal MRI datasets used for the simulations showing large range of anatomy. The age at exam are: (a) 28 weeks, (b) 32.6 weeks, (c) 34.3 weeks and (d) 42.6 weeks. . . . . . . . . . . . . . . . . . . Each of the 24 simulations is represented by a point. For all cases, final RMS errors remained below 1mm (please note the difference of scaling between starting RMS and final RMS). . . . . . . . . . . . . . . . . 19 7 High Resolution fetal MR image (2nd row) reconstructed from low resolution images (1st row, coronal acquisition). 3rd row: Surface rendering of semiautomatic brain surface segmentation performed on original low resolution image and on high resolution reconstructed image. . . . . . . . . . . . . . . . . . . . 20 8

			17
	5	(a) Original isotropic high resolution premature neonatal brain MRI; (b) Simu-
		lated low resolution volume using sudden motion and corresponding high reso-
		lution reconstructed volume; (c) Simulated low resolution volume using smooth
		motion and corresponding high resolution reconstructed volume. The maximum
		magnitude of the simulated motion was 10 (see text for more details) . . . . .	18
	6	Simulation results for premature neonate MR images: starting RMS errors ver-
		sus final RMS errors in mm using two different simulated motions (sudden mo-
		tion and smooth motion).

http://rview.colin-studholme.net


Hassan Saneifar
email: saneifar@lirmm.fr

Stéphane Bonniol
email: stephane.bonniol@satin-ip.com

Anne Laurent
email: laurent@lirmm.fr

Pascal Poncelet
email: poncelet@lirmm.fr

Mathieu Roche
email: mroche@lirmm.fr

MINING FOR RELEVANT TERMS FROM LOG FILES

Keywords: Natural Language Processing, Information Retrieval, Terminology Extraction, Terminology Ranking, Log Files

The Information extracted from log files of computing systems can be considered one of the important resources of information systems. In the case of Integrated Circuit design, log files generated by design tools are not exhaustively exploited. The logs of this domain are multi-source, multi-format, and have a heterogeneous and evolving structure. Moreover, they usually do not respect the grammar and the structures of natural language though they are written in English. According to features of such textual data, applying the classical methods of information extraction is not an easy task, more particularly for terminology extraction. We have previously introduced EXTERLOG approach to extract the terminology from such log files. In this paper, we introduce a new developed version of EXTERLOG guided by Web. We score the extracted terms by a Web and context based measure. We favor the more relevant terms of domain and emphasize the precision by filtering terms based on their scores. The experiments show that EXTERLOG is well-adapted terminology extraction approach from log files.

INTRODUCTION

In many applications, computing systems generate reports automatically. These digital reports, also known as system logs, represent the major source of information on the status of systems, products, or even the causes of problems that can occur. Although log files are generated in every field of computing, the characteristics of these logs, particularly the language, structure and context, differ from system to system. In some areas, such as Integrated Circuit (IC) design systems, the log files are not systematically exploited in an effective way whereas in this particular field, the log files generated by IC design tools, contain essential information on the condition of production and the final products. In this context, a key challenge is to provide approaches which consider the multi-source, heterogeneous and scalable structures of log files as well as their special vocabulary. Furthermore, although the contents of these logs are similar to texts written in Natural Language (NL), they comply neither with the grammar nor with the NL structure. Therefore, In order to extract information from the logs, we need to adapt Natural Language Processing (NLP) and Information Extraction (IE) techniques to the specific characteristics of such textual data. Another key challenge is evaluation of results. In fact, according to the particularity of such data, and then due to the high noise ratio in results, the classic evaluation methods are not necessarily relevant. To emphasize the precision of results as a must according to the accuracy of context, we have to define the noise filtering method which comply with the particularity of such data.

The creation of a domain ontology is a primordial need for our future work on information extraction from log files. Defining the vocabulary of domain is one of the first steps of building an ontology. To analyze vocabulary and lexical structure of a corpus, extraction of domain terminology is one of the most important phases. We thus aim at extracting the terminology of log files. The extracted terms will be used in the creation of domain ontology in our future works. Also, we will use extracted terms to study the different lexical structures of different logs in order to enrich our information extraction methods. In this paper, we introduce a new version of our approach EX-TERLOG (EXtraction of TERminology from LOGs), previously presented in [START_REF] Saneifar | Terminology extraction from log files[END_REF], that is developed to extract the terminology from these log files. In this approach, we study how to adapt the existing terminology extraction methods to the particular and heterogeneous features of log files. We also present in this paper a filtering method of extracted terms based on a ranking score in order to emphasize the precision of extracted relevant terms.

In Sect. 2, we detail the utility of building domain ontology and thus the terminology extraction in our context and the special features and difficulties of this domain. Our approach EXTERLOG is developed in Sect. 3. Section 4 describes and compares the various experiments that we performed to extract terms from the logs and specially to evaluate the precision of EXTERLOG.

CONTEXT

Today, digital systems generate many types of log files, which give essential information on the systems. Some types of log files, like network monitoring logs, web services interactions or web usage logs are widely exploited [START_REF] Yamanishi | Dynamic syslog mining for network failure monitoring[END_REF] [START_REF] Facca | Mining interesting knowledge from weblogs: a survey[END_REF]. These kinds of log files are based on the management of events. That is, the computing system, which generates the log files, records the system events based on their occurring times. The contents of these logs comply with norms according to the nature of events and their global usage (e.g. web usage area).

However, in some areas such as integrated circuit design systems, rather than being some recorded events, the generated log files are digital reports on configuration, condition and states of systems. The aim of the exploitation of these log files is not to analyze the events but to extract information about system configuration and especially about the final product's condition. Hence, log files are considered an important source of information for systems designed to query and manage the production. Information extraction in log files generated by IC design tools has an attractive interest for automatic management and monitoring of IC production. However, several aspects of these log files have been less emphasized in existing methods of text mining and NLP. These specific characteristics raise several challenges that require more research.

IE & Log Files

To use these logs in an information system, we must implement information extraction methods which are adapted to the characteristics of these logs. Moreover, these features explain why we need a domain ontology to extract information from the log files.

In the field of integrated circuits design, several levels need to be considered. At every level, different design tools can be used which make the generated log files the multi-source data. Despite the fact that the logs of the same design level report the same information, their structures can differ significantly depending on the design tool used. Specifically, each design tool often uses its own vocabulary to report the same information. In the verification level, for example, we produce two log files (e.g. log "A" and log "B") by two different tools. The information about, for example, the "Statement coverage" will be expressed as follows in the log "A":

TOTAL

COVERED PERCENT Lines 10 11 12 statements 20 21 22 But the same information in the log "B", will be disclosed from this single line: EC: 2.1% As shown above, the same information in two log files produced by two different tools is represented by different structures and vocabulary. Moreover, design tools evolve over time and this evolution often occurs unexpectedly. Therefore, the format of the data in the log files changes, which make the automatic management of data difficult. The heterogeneity of data exists not only between the log files produced by different tools, but also within a given log file. For example, the symbols used to present an object, such as the header for tables, change in a given log. Similarly, there are several formats for punctuation, the separation lines, and representation of missing data. Therefore, we need intelligent and generalized methods, which can be applied at the same time on different logs (multi-source textual data) which have the multiformat and heterogeneous data. These methods must also take into account the variable vocabulary of these logs. To generalize the extraction methods, we thus need to identify the terms used by each tool in order to create the domain ontology. This ontology allows us to better identify equivalent terms in the logs generated by different tools and so to reduce the heterogeneity of data. For instance, to check "Absence of Attributes" as a query on the logs, one must search for the following different sentences in the logs, depending on the version and type of design tool used:

• "Do not use map to module attribute" • "Do not use one cold or one hot attributes" • "Do not use enum encoding attribute"

Instead of using several patterns, each one adapted for a specific sentence, by associating the words "map to module attribute", "one hot attributes" and "enum encoding attribute" to the concept "Absence of Attributes", we use a general pattern that expands automatically according to different logs using the domain ontology. The ontology-driven expansion of query is studied in many works, see [START_REF] Voorhees | Query expansion using lexicalsemantic relations[END_REF]) [START_REF] Dey | Ontology aided query expansion for retrieving relevant texts[END_REF]. The ontology will allow us to better identify equivalent terms in the logs generated by different tools. Several approaches are based on the domain ontology to better guide the information extraction [START_REF] Even | Extraction d'informations à partir de corpus dégradés[END_REF]. An ontology also defines the common vocabulary of a domain [START_REF] Mollá | Question answering in restricted domains: An overview[END_REF]. In our context, the domain ontology allows us to categorize the terms associated with a concept sought on the logs. The creation of ontology requires a lexical analysis of a corpus to identify the terms of the domain. We hence seek to identify the terms of the logs of every design tool. We will then look at these terms in order to make the correspondence between them and to create the domain ontology. Thus, we aim at studying the extraction of terminology from log files.

Also, the language used in these logs is a difficulty that affects the methods of information extraction. Although the language used in these logs is English, the contents of these logs do not usually comply with "classic" grammar. Moreover, there are words that are often constituted from alphanumeric and special characters.

Due to these specific characteristics of log files, the methods of NLP, including the terminology extraction, developed for texts written in natural language, are not necessarily well suited to the log files.

Terminology Extraction Background

The extraction of domain terminology from the textual data is an essential task to establish specialized dictionary of a domain [START_REF] Roche | EXIT: Un système itératif pour l'extraction de la terminologie du domaine à partir de corpus spécialisés[END_REF]. The extraction of co-occurring words is an important step in identifying the terms. To identify the co-occurrences, some approaches are based on syntactic techniques which rely initially on the grammatical tagging of words. The terminological candidates are then extracted using syntactic patterns (e.g. adjective-noun, noun-noun). We develop the grammatical tagging of log files using our approach EXTERLOG in Sect. 3.2. Bigrams1 are used in [START_REF] Meng Tan | The use of bigrams to enhance text categorization[END_REF] as features to improve the performance of the text classification. The series of three words (i.e. trigrams) or more is not always essential [START_REF] Grobelnik | Word sequences as features in textlearning[END_REF]. The defined rules and grammar are used in [START_REF] David | De la nécessité d'une approche morpho-syntaxique en analyse de textes[END_REF] in order to extract the nominal terms as well as to evaluate them. The machine learning methods based on Hidden Markov Models (HMMs) are used in [START_REF] Collier | Automatic acquisition and classification of terminology using a tagged corpus in the molecular biology domain[END_REF] to extract terminology in the field of molecular biology. EXIT, introduced by [START_REF] Roche | EXIT: Un système itératif pour l'extraction de la terminologie du domaine à partir de corpus spécialisés[END_REF]) is an iterative approach that finds the terms in an incremental way. A term found in an iteration is used in the next one to find more complex terms. Some works try to extract the co-occurrences in a fixed size window (normally five words). In this case, the extracted words may not be directly related [START_REF] Lin | Extracting collocations from text corpora[END_REF]. XTRACT avoids this problem by considering the relative positions of co-occurrences. XTRACT is a terminology extraction system, which identifies lexical relations in the large corpus of English texts [START_REF] Smadja | Retrieving collocations from text: Xtract[END_REF]. SYN-TEX, proposed by [START_REF] Bourigault | Approche linguistique pour l'analyse syntaxique de corpus[END_REF], performs syntactic analysis of texts to identify the names, verbs, adjectives, adverbs, the noun phrases and verbal phrases. It analyses the text by applying syntactic rules to extract terms.

As described above, we have previously studied the extraction of terminology based on identifying the co-occurring words without using the syntactic patterns from log files (see [START_REF] Saneifar | Terminology extraction from log files[END_REF]). As explained in [START_REF] Saneifar | Terminology extraction from log files[END_REF], the terminology extraction based on syntactic patterns is quite relevant to the context of log files. We shown that the accuracy of terms extracted based on syntactic patterns is indeed higher than the precision of bigrams extracted without such patterns. Despite the fact that normalization and tagging the texts of logs is not an easy task, our previous experiments show that an effort in this direction is useful in order to extract quality terms. But according to the need of high accuracy in this domain and the fact that manual validation of terms by an expert is expensive, we develop here the automatic evaluation phase of EXTERLOG. This evaluation of terms is detailed in Section 3.4.

The statistical methods used are generally associated with syntactic methods for evaluating the adequacy of terminological candidates [START_REF] Daille | Conceptual structuring through term variations[END_REF]. These methods are based on statistical measures such as information gain to validate an extracted candidate as a term. Among these measures, the occurrence frequency of candidates is a basic notion. However, these statistical methods are not relevant to be applied on the log files. Indeed, statistical approaches can cope with high frequency terms but tend to miss low frequency ones [START_REF] Evans | Noun-phrase analysis in unrestricted text for information retrieval[END_REF]. According to the log files described above, the repetition of words is rare. Each part of a log file contains some information independent from other parts. In addition, it is not reasonable to establish a large corpus of logs by gathering log files generated by the same tool at the same level of design. Since, it just results the redundancy of words. Evaluation of terms based on some other resources like as web is studied by many works. The Web, as a huge corpus, is more and more used in NLP methods specially in validation of results. However, in our context, we study the corpus of a very specialized domain. The terms used in this domain are the specialized terms and not frequently seen on the Web. Then, we could not use the classic statistical measures based on simple frequencies of terms in corpus in order to give a score to every extracted term. Furthermore, our approach aims at reducing the noise ratio in results, thus emphasizing the precision, by filtering the extracted terms using a web based statistical measures which considers in the same time the context of log files. We detail this aspect in Sect. 3.4.

A lot of works compare the different techniques of terminology extraction and their performance. But most of these studies are experimented on textual data, which are classical texts written in natural language. Most of the corpus that are used are structured in a consistent way. In particular, this textual data complies with the grammar of NL. However, in our context, the characteristics of logs (such as not to comply with natural language grammar, their heterogeneous and evolving structures (cf. Sect. 2)) impose an adaptation of these methods to ensure that they are relevant for the case of log files.

EXTERLOG: EXtraction of TERminology from LOGs

Our approach, EXTERLOG, is developed to extract the terminology in the log files. The extraction process involves normalization, preprocessing of log files and grammatical tagging of word in order to extract the terms. EXTERLOG contains also a filtering phase of extracted terms based on a scoring measure.

Preprocessing & Normalization

The heterogeneity of the log files is a problem, which can affect the performance of information extraction methods. In order to reduce the heterogeneity of data and prepare them to extract terminology, we apply a series of preprocessing and normalization on the logs. Given the specificity of our data, the normalization method, adapted to the logs, makes the format and structure of logs more consistent. We replace the punctuations, separation lines and the headers of the tables by special characters to limit ambiguity. Then, we tokenize the texts of logs, considering that certain words or structures do not have to be tokenized. For example, the technical word "Circuit4-LED3" is a single word which should not be tokenized into two words "Circuit4" and "LED3". Besides, we distinguish automatically the lines representing the header of tables from the lines which separate the parts. After the normalization of logs, we have less ambiguity and less common symbols for different concepts. This normalization makes the structure of logs produced by different tools more homogeneous.

Grammatical Tagging

Grammatical tagging (also called part-of-speech tagging) is a method of NLP used to analyse the text files which aims to annotate words based on their grammatical roles. In the context of log files, there are some difficulties and limitations for applying a grammatical tagging on such textual data. Indeed, the classic techniques of POS tagging are developed using the standard grammar of natural language. In addition, they are normally trained on texts written in a standard natural language, such as journals. Therefore, they consider that a sentence ends with a fullstop, for example, which is not the case in the log files that we handle. More specifically, in these log files, sentences and paragraphs are not always well structured. Besides, there are several constructions that do not comply with the structure of sentences in natural language. To identify the role of words in the log files, we use BRILL rule-based part-of-speech tagging method [START_REF] Brill | A simple rule-based part of speech tagger[END_REF]. Since existing taggers like BRILL are trained on general language corpora, they give inconsistent results on the specialized texts. [START_REF] Amrani | A semi-automatic system for tagging specialized corpora[END_REF] propose a semi-automatic approach for tagging corpora of speciality. They build a new tagger which corrects the base of rules obtained by BRILL tagger and adapt it to a corpus of speciality. In the context of log files, we need also to adapt BRILL tagger just as in [START_REF] Amrani | A semi-automatic system for tagging specialized corpora[END_REF]. We thus adapted BRILL to the context of log files by introducing the new contextual and lexical rules. Since, the classic rules of BRILL, which are defined according to the NL grammar, are not relevant to log files. For example, a word beginning with a number is considered a "cardinal" by BRILL. However, in the log files, there are many words like 12.1vSo10 that must not be labelled as "cardinal". Therefore, we defined the special lexical and contextual rules in BRILL. The structures of log files can contribute important information for extracting the relevant patterns in future works. Therefore, we preserve the structure of files during grammatical tagging. We introduce the new tags, called "Document Structure Tags", which present the different structures in log files. For example, the tag "\TH" represents the header of tables or "\SPL" represents the lines separating the log parts. The special structures in log files are identified during normalization by defined rules. Then, they are identified during tagging by the new specific contextual rules defined in BRILL. We finally get the logs tagged by the grammatical roles of words and also by the labels that determine the structure of logs.

Extraction of Co-occurrences

We extract the co-occurrences in the log files respecta defined part-of-speech syntactic pattern. We call the co-occurrences extracted using syntactic pattern "POS-candidates"2 . The syntactic patterns determine the adjacent words with the defined grammatical roles. The syntactic patterns are used in [START_REF] Daille | Conceptual structuring through term variations[END_REF] and [START_REF] Bourigault | Approche linguistique pour l'analyse syntaxique de corpus[END_REF] to extract terminology. As argued in [START_REF] Daille | Conceptual structuring through term variations[END_REF], the base structures of syntactic patterns are not frozen structures and accept variations. According to the terms found in our context, the syntactic patterns that we use to extract the "POS-candidates" from log files are: "\JJ -\NN" (Adjective-Noun), "\NN -\NN" (Noun-Noun).

These extracted terms at this phase must be scored to favor the most relevant terms of the domain.

Filtering of Candidates

All the extracted terms are not necessarily the relevant terms of the domain. Because of some huge log files and the large vocabulary of the logs, there exists so many extracted terms. Also, according to the particular features of such data, in spite of adapted normalization and tagging methods that we used, there exists some noise (no relevant terms) in the extracted terms. Moreover, we are focused on a specialized domain where just some terms are really bidden to the domain's context. Thus, we score, rank and then filter the extracted terms in order to favor the most relevant terms according to the context. The statistical measures are often used in terminology extraction field to evaluate the terms (see [START_REF] Daille | Study and Implementation of Combined Techniques for Automatic Extraction of Terminology[END_REF]). The following ones are the most widely used.

Mutual Information. One of the most commonly used measures to compute a sort of relationship between the words composing what is called a cooccurrence is Church's Mutual Information (MI) [START_REF] Church | Word association norms, mutual information, and lexicography[END_REF]. The simplified formula is the following where nb designates the number of occurrences of words and couples of words:

MI(x, y) = log 2 nb(x, y) nb(x)nb(y)
Cubic Mutual Information. The Cubic Mutual Information is an empirical measure based on MI, that enhances the impact of frequent co-occurrences, something which is absent in the original MI [START_REF] Daille | Approche mixte pour l'extraction automatique de terminologie : statistiques lexicales et filtres linguistiques[END_REF].

MI3(x, y) = log 2 nb(x, y) 3 nb(x)nb(y)
This measure is used in several works related to noun or verb terms extraction in texts [START_REF] Roche | AcroDef : A quality measure for discriminating expansions of ambiguous acronyms[END_REF].

Dice's Coefficient. An interesting quality measure is Dice's coefficient [START_REF] Smadja | Translating collocations for bilingual lexicons: A statistical approach[END_REF]. It is defined by the following formula based on the frequency of occurrence.

Dice(x, y)

= 2 × nb(x, y) nb(x) + nb(y)
These measures are based on the occurrence frequencies of terms in corpus. Scoring the terms based on frequencies of terms in corpus of logs is not a relevant approach in our context. As we have already explained, the techniques based on frequency of terms in a corpus (e.g. pruning terms having low frequency) are not relevant to this context as a representative term does not necessarily have a high frequency in log files.

That is why we score the terms according to their frequencies on the Web as a large corpus where frequency of a term can be representative. Working on a specialized domain, we have bias scores based on the simple count of occurrences of a term on Web. Indeed, on Web, we capture occurrences of terms regardless of the context in which they are seen. Thus, we should consider only the occurrences of terms on web which are situated in the IC design context. We use therefore an extension of described measures called AcroDe f . AcroDe f is a quality measure where context and Web resources are essential characteristics to be taken into account (see [START_REF] Roche | AcroDef : A quality measure for discriminating expansions of ambiguous acronyms[END_REF]). The below formulas define the AcroDe f measures, respectively based on MI and Cubic MI.

AcroDe f MI (a j ) = nb( T n i=1 a j i +C) ∏ n i=1 nb(a j i +C|a j i ∈ M stop-words ) where n ≥ 2 AcroDe f MI3 (a j ) = nb( T n i=1 a j i +C) 3 ∏ n i=1 nb(a j i +C|a j i ∈ M stop-words )
where n ≥ 2

In AcroDe f , the context "C" is represented as a set of significant words. The nb function used in the preceding measures represents the number of pages provided by the search engine to given query. Then nb(a j i + C) returns the number of pages applying query a j i + C which means all words of the term a j in addition to those of context C. In our case, for example, for a term x j like "atpg patterns" consisting of two words (so i = 2), nb(at pg T patterns +C) is the number page returned by applying query "atpg pattern" AND C on a search engine, where C is a set of words representing the context. The AcroDe f Dice formula based Dice's formula is written as follows:

{a j i +C|a j i ∈ M stop-words } i∈[1,n] × nb( T n i=1 a j i +C) ∑ n i=1 nb(a j i +C|a j i ∈ M stop-words )
where n ≥ 2

In [START_REF] Roche | AcroDef : A quality measure for discriminating expansions of ambiguous acronyms[END_REF], "C" is represented as a set of significant words (e.g. encryption, information and code to represent the Cryptography context). The authors made some experiments with different number of words represented as context. In all cases, authors use "AND" search engine operator between the words of context. That is, they request the pages containing all words in "C". However, working on a very specialized domain which contains some more specific sub domains, we do not get the best results by using just an "AND" operator for the words of context.

To specify the words which represent the context of log files, we build a corpus of documents including the reference documents of Integrated Circuit design tools and tree other domains documents. We rank the words of corpus by using tf-idf measure (see [START_REF] Salton | Term weighting approaches in automatic text retrieval[END_REF]). Tf-idf gives higher score to the frequent words of a domain which are not frequent in other ones. Then, we choose the first five words (ranked in tf-idf order) of IC design documents as representing word of the context. As argued above, we look for web pages containing a given term and two or more words of context (using the operators OR and AND). Finally, the extracted terms are ranked by means of their AcroDe f scores. We favor the most ranked terms by filtering those having most low AcroDe f scores.

EXPERIMENTS

In all experiments the log corpus is composed of logs of five levels of IC design. For each level, we considered two logs generated in different conditions of design systems. The size of the log corpus is about 950 KB. All the experiments are done using the Google search engine.

Evaluation of Terms by AcroDe f

The extracted terms by EXTERLOG from the log files are so numerous which make difficult the final validation by experts of domain. Thus, we experiment by taking a sample of extracted terms. We select the 200 more frequent terms extracted from logs of every IC design level. Note that in few levels, there exists less than 200 terms. The taken sample consists of 700 terms at all.

To filter the extracted terms from log files, we rank them by AcroDe f (cf. 3.4). To apply AcroDe f , we determine the context words as described in Sect. 3.4. Then, we use the Google search engine to capture the number of pages containing a given term and two or more words of context. Suppose a given term like "CPU time" where C i i ∈ {1 -5} are the context words, the query used in Google search engine is "CPU time" AND C 1 AND C 2 OR C 3 OR C 4 OR C 5 .

Once AcroDe f scores are calculated, we rank the terms based on their AcroDe f . The more AcroDe f has a higher value, the more the term is representative (seen) in our context. Then, we select the most rated terms in the goal of emphasizing the precision by reducing the noise ratio (no relevant terms) in results. Once the terms filtered, we asked two domain experts to evaluate remain terms in order to determine the precision of our terminology extraction approach from log files. First extracted terms are tagged by a domain expert as relevant or not relevant according to the context and their usefulness in the logs. Then, another expert reviewed the tagged terms by the first expert. Then, the precision is calculated as percentage of remain terms (after filtering by AcroDe f scores) which are tagged as "relevant" by experts. We also calculate F-score as the harmonic mean of precision and recall to measure our approach accuracy.

F -score = 2 * (Precision * Recall) Precision + Recall
We experiment with different numbers of the most ranked terms as the ones which remain after filtering. That is, suppose n terms in sample, we filter the terms by selecting the m most ranked terms by AcroDe f score. Table 1 shows the results of filtering with different m as threshold of filtering. In m = 500, for example, we take the 500 most ranked terms. In m = 700, we do not actually filter any terms. Thus, the recall is equal to 100%. The results show that by means of our filtering approach, we favor more relevant terms and emphasize the precision.

CONCLUSION & FUTURE WORK

In this paper, we describe a particular type of textual data: log files generated by tools for integrated circuit design. Since these log files are multisource, multi-format, heterogeneous, and evolving textual data, the NLP and IE methods are not necessarily well suited to extract information.

To extract domain terminology, we extracted the co-occurrences. For that, we apply the specific preprocessing, normalization and tagging methods. To reduce the noise ratio in extracted terms and favor more relevant terms of this domain, we score terms using a Web and context based measure. Then, we select the most ranked terms by filtering based on score of terms. The experiments show that our approach of terminology extraction from log files, EXTERLOG, can achieve an F-score equal to 0.79 after filtering of terms.

To improve the performance of terminology extraction, we will develop our normalization method. Given the importance of accurate grammatical tagging, we will improve the grammatical tagger. Finally, we plan to take into account the terminology extracted using our system to enrich the patterns of information extraction from log files.

  Precision = |Terms relevant ∩ Terms remained | |Terms remained | Terms relevant = terms validated by expert in sample scale Terms remained = terms remained after filtering We calculate the recall as the percentage of all relevant terms (tagged by experts in sample scale) which remain after filtering. Recall = |Terms relevant ∩ Terms remained | |Terms relevant | Terms validated = terms validated by expert in sample scale Terms remained = terms remained after filtering

Table 1 :

 1 Precision, Recall, and F-score of terms in each level of filtering based on AcroDe f MI score Table 2 demonstrates the same filtering results but based on AcroDe f MI3 scores. Table 3 shows the same experiments using AcroDe f Dice as scoring measure. According to results, AcroDe f MI3 is more relevant to score the extracted terms in our context. By using AcroDe f MI3 we reach better precision. That is, AcroDe f MI3 score better the relevant terms of domain.

	m	Precision Recall F-score
	200	80 %	38 %	52 %
	300	78 %	56 %	65 %
	400	74 %	71 %	72 %
	500	72 %	87 %	79 %
	600	66 %	95 %	78 %
	700	59 %	100 %	74 %

Table 2 :

 2 Precision, Recall, and F-score of terms in each level of filtering m based on AcroDe f MI3 score

	m	Precision Recall F-score
	200	85 %	41 %	55 %
	300	79 %	57 %	67 %
	400	74 %	72 %	73 %
	500	72 %	87 %	79 %
	600	66 %	95 %	78 %
	700	59 %	100 %	74 %

Table 3 :

 3 Precision, Recall, and F-score of terms in each level of filtering m based on AcroDe f Dice score

N-grams are defined as the series of any "n" words.

POS: Part-Of-Speech