Fast, Accurate, and Automatic Extraction of the Modified Talairach Cortical Landmarks from Magnetic Resonance Images

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1 Fast, Accurate, and Automatic Extraction of the Modified Talairach Cortical Landmarks from Magnetic Resonance Images Qingmao Hu, Guoyu Qian, and Wieslaw L. Nowinski Magnetic Resonance in Medicine 53: (2005) The Talairach transformation is the most prevalent way to normalize brains and is hindered by, among others things, a lack of automatic determination of cortical landmarks. An algorithm to locate the modified Talairach cortical landmarks in three steps is proposed: determination of the three planes containing the landmarks; segmentation of the planes based on range-constrained thresholding and morphologic operations; and local refinement of the segmentation to locate the landmarks. The algorithm has been validated against 62 T 1 -weighted and SPGR MR diversified data sets. For each data set, it takes less than 2 s on a Pentium 4 to extract all six landmarks. The average landmark location errors are below 0.9 mm. The algorithm is robust due to incorporation of anatomic knowledge. A low computational cost results from processing of three 2D images and employing only simple operations like thresholding, basic morphologic operations, and distance transform. Magn Reson Med 53: , Wiley-Liss, Inc. Key words: modified Talairach landmarks; Talairach transformation; segmentation; MRI The Talairach Tournoux brain atlas, whether in print (1) or electronic (2) version, is one of the most common atlases used in stereotactic and functional neurosurgery, human brain mapping, and neuroradiology. The Talairach transformation (1), despite its limitations (3), is still the most preferred way to warp the atlas against data and to normalize brains. It is determined by the midsagittal plane (MSP), anterior commissure (AC), and posterior commissure (PC), and six Talairach cortical landmarks. We have previously proposed an algorithm to extract the MSP (4). The automatic identification of the AC and PC has been studied, e.g., in Ref. (5), while the Talairach cortical landmarks are usually determined manually (6). The Talairach cortical landmarks are the extreme cortical points, i.e., most anterior, most posterior, leftmost, rightmost, most inferior, and most superior. Nowinski (7) modified the Talairach landmarks to be conceptually equivalent to the original ones while facilitating rapid and automated calculation of the Talairach transformation. Automatic identification of either standard or modified Talairach cortical landmarks from MR neuroimages is difficult due to the inherent nature of these images: noise, gray level intensity inhomogeneity, partial volume effect, artifacts, stereotactic frames, sagittal sinus/meninges connected to the cortex, closeness of the cortex to the optic nerves both spatially and in gray levels. This paper focuses on fast, accurate, and automatic extraction of the six modified Talairach cortical landmarks based on anatomic knowledge and range-constrained thresholding. The algorithm has been tested successfully against 372 (62 6) modified landmarks of 62 T 1 -weighted (T 1 W) and SPGR simulated and real morphologic MR data sets with various levels of noise, intensity inhomogeneity, and artifacts. It takes less than 2sonaPentium 4, 2.6-GHz CPU, and its average landmark location error is smaller than 0.9 mm. METHODS The coordinate system (xyz) corresponds to the standard radiologic convention: x runs from the subject s right to left, y from anterior to posterior, and z from superior to inferior. Each 2D image has additionally its own coordinate system (uv) with u being horizontal and v vertical. For an axial slice, the u coordinate is the same as the x coordinate and the v coordinate is the same as the y coordinate. For a coronal slice, the v coordinate is the same as the z coordinate. Materials A total of 62 MR data sets were used including 24 clinical T 1 W and SPGR data sets from Singapore and Japan (1.5 and 3 T), 20 T 1 W normal data sets from the Internet Brain Segmentation Repository (IBSR) ( edu/ibsr), and 18 T 1 W Brainweb phantom data sets ( All data sets are eight-bit and not corrected by any preprocessing. The phantom data sets are isotropic while the rest are not. Modified Talairach Cortical Landmarks The standard Talairach landmarks contain the subcortical (the AC and PC) and cortical landmarks. Each cortical landmark is identified by three coordinates and typically skull stripping is required to facilitate identification. Modified Talairach landmarks are introduced (7) to facilitate calculations. Each modified Talairach cortical Biomedical Imaging Lab, Agency for Science, Technology and Research, 30 landmark is defined by a single coordinate only and is Biopolis Street, Matrix, Singapore. *Correspondence to: Qingmao Hu, Biomedical Imaging Lab, Agency for Science, Technology and Research, 30 Biopolis Street, Matrix, planes. The AC PC plane passes through the AC and PC located on a well-defined plane. We introduce three Singapore. huqm@bii.a-star.edu.sg and is perpendicular to the MSP. The VAC plane is perpendicular to both the MSP and the AC PC planes and Received 5 August 2004; revised 29 October 2004; accepted 29 October DOI /mrm passes through the AC. The VPC plane is parallel to the Published online in Wiley InterScience ( VAC plane and passes through the PC. The anterior and 2005 Wiley-Liss, Inc. 970

2 Extraction of the Modified Talairach Landmarks 971 posterior extents of the brain are defined by the A and P landmarks, respectively, lying on the intersection of the AC PC and MSP planes. The left and right extents of the brain are defined by the L and R landmarks, respectively, lying on the intersection of the AC PC and VPC planes. The superior (inferior) extent of the brain is defined by the S (I) landmark lying on the intersections of the VPC (VAC) and MSP planes. Algorithm The six modified Talairach cortical landmarks are determined in three steps. First, the three planes containing the landmarks are calculated. For each plane, a threshold is determined based on range-constrained thresholding. Then, the three planes are segmented with the chosen thresholds and morphologic operations. Finally, the segmentation is refined to compensate for the influence of sagittal sinus/meninges, optic nerves, and the partial volume effect. The AC PC, VAC, and VPC planes are extracted from the original volumetric data. The voxels on these planes are resampled to 1 mm in x, y, and z directions by trilinear interpolation. Three types of voxels are differentiated: background, foreground, and object. Thresholding yields foreground and background voxels. When a voxel is either white matter (WM) or gray matter (GM), it is an object voxel; otherwise it is a nonobject voxel. Range-Constrained Thresholding The existing thresholding methods (8,9) lack the mechanisms to incorporate knowledge about the images to be segmented and thus handle poorly the inherent nature of the neuroimages like noise and inhomogeneity. We propose range-constrained thresholding, which explicitly incorporates the knowledge into the segmentation scheme and consists of three steps. First, the region of interest (ROI) is determined in the image. Then, within this ROI a range in the corresponding histogram is estimated by knowledge, which represents the minimum and maximum bounds that the background proportion can take. Finally, the threshold is chosen to maximize the between-class variance (8) within this range. Let h(i) denote the frequency of gray level r i. The following steps yield the optimum threshold. 1. Specify two percentages H b l and H b h, corresponding to the lower and upper frequency bounds of the background in the ROI based on prior knowledge or tests; 2. Calculate r low (r high ), which is the gray level corresponding to the background lower (upper) bound H b l ( H b h ): r low min i i H i H b l }; r high min i i H i H b h }; and r H(i) i j 0 h( j). 3. Calculate the between-class variance with respect to the variable r k : Pr C1 D C1 Pr C2 D C2, [1] where r k falls within (r low, r high ), Pr(C1) rk rlow h(i), Pr(C2) rhigh rk 1 h(i), D(C1) ( 0 T ) 2, D(C2) 1 T 2, T rhigh rlow i h(i), 0 rk rlow i h(i), 1 rhigh rk 1 i h(i). FIG. 1. Identification of the A, P, L, and R landmarks from the AC PC image: (a) the original AC PC image; (b) region of interest (ROI); (c) segmented AC PC image; and (d) two horizontal (vertical) lines passing through the extracted A and P (L and R) landmarks. The optimum threshold is the r k maximizing formula [1] for given H l b and H h b. Determination of the A, P, L, and R Landmarks The A, P, L, and R landmarks are localized through segmentation of the AC PC plane. First, the AC PC plane is binarized with an optimum threshold derived from rangeconstrained thresholding. Then, the connections between brain and non-brain tissues are broken through morphologic opening with a square structuring element (SE) of 5 mm, while small brain fragments are maintained using a smaller SE of 3 mm. Non-brain tissues like skull, scalp, and bones are removed according to their small distance to the background. Then, the partial volume effect and morphologic opening are compensated to refine the segmentation. The following steps yield the A, P, L, and R landmarks. 1. Find the voxels enclosed by the skull and take them as the ROI, Fig. 1b. This is done in three steps (10): binarizing the AC PC plane using a histogram-based thresholding, morphologic closing with a 3 3 SE, and identification of the largest connected component and filling the holes in it. 2. Determine the optimum threshold within the ROI given H l b 14%, H h b 28%.

3 972 Hu et al. The two percentages are derived from analyzing the ground-truth segmentation of the 18 phantom data sets and 20 IBSR data sets (the background proportions largely fall within the range of 16 25%). The threshold maximizing formula (1) is denoted Segment the AC PC plane by the following substeps. (a) Perform distance transformation of the ROI. This transformation approximates the Euclidean distance between a foreground voxel and its nearest background voxel. Obviously, sagittal sinus/meninges/skull voxels have small distances which facilitate non-brain tissue removal. Denote the maximum distance of the ROI as max- DSkull. The minimum and maximum distances of each connected component within the ROI can be found and denoted as mind and maxd, respectively. (b) Binarize the image within the ROI (a voxel is set to the foreground if its intensity is bigger than 1 and to the background otherwise). The binarized image is denoted AP1. (c) In AP1 perform morphologic opening with 3 3SE to get AP2. This breaks connections between the sagittal sinus/meninges/skull and cortex if the foreground voxels connecting them cannot fill completely at least a 3 3 square window in AP1. (d) In AP1 perform morphologic opening with 5 5SE to get AP3. This breaks connections between the sagittal sinus/meninges/skull and cortex if the foreground voxels connecting them cannot fill completely at least a 5 5 square window in AP1. (e) Find the connected foreground components of AP3. A foreground component is judged as an object component when its minimum distance is bigger than 10 mm or its maximum distance is bigger than maxdskull/2. This distance thresholding removes non-brain fragments not connected to the brain tissues as they have small distances. (f) The foreground difference between AP2 and AP3 is calculated and grouped into connected components. Those foreground components are to be added to the object components if they are not meninges by a simple shape analysis as follows. In an axial slice, meninges are close to the skull and have a shape similar to the outline of the skull. As meninges are also quite thin, they should have similar distance to the background. So when (maxd mind) is smaller than 0.1 times the number of voxels of this component, the foreground component is highly probable to be the meninges and is judged as background; otherwise it is classified as an object component. 4. Restore object voxels around the object boundaries due to the morphologic opening when their gray level is bigger than the gray level threshold 1. The restoration is not performed around the most anterior and most posterior parts of the line connecting the AC and PC to avoid including the sagittal sinus/meninges. Suppose the minimum and maximum v coordinates of object voxels are minv and maxv, respectively, and the coordinates of the AC and PC are (acu, acv) and (pcu, pcv) on the AC PC plane, respectively. The restoration is not carried out in the following two rectangular regions: u acu 10 mm and v minv 3 mm [2] u pcu 10 mm and v maxv 3 mm. [3] 5. Restore object voxels due to the partial volume effect. The basic idea is to check whether the gray level is monotonically decreasing from the cortical surface to the background and the cortex proportion of the immediate nonobject voxel is at least 0.5. Denote the coordinates of the object voxel with the minimum u coordinate as (u min, v l ). The average gray levels at voxels (u min, v l ), (u min 1,v l ), (u min 2,v l ) are calculated and denoted G 0, G 1, and G 2, respectively, where the average gray level at (u,v) is the gray level average of the AC PC plane at (u, v 1), (u, v), and (u, v 1). In T 1 W or SPGR MR images, GM is brighter than CSF. Suppose the GM proportion of voxel at (u min 1,v l ) is gmp, then G 1 gmp G 0 1 gmp G 2. When gmp is not less than 0.5, (2G 1 )/ (G 2 G 0 ) is at least 1.0 and the partial volume effect is compensated by subtracting 1 mm from u min ; otherwise u min remains unchanged. Modifications of u and minimum/maximum v coordinates of the object voxels can be done in a similar way. 6. The v coordinates of the A and P landmarks are the minimum and maximum v coordinates, respectively, of all object voxels. Similarly, the u coordinates of the R and L landmarks are the minimum and maximum u coordinates of all object voxels, respectively. Figure 1a, b, c, and d shows the AC PC plane of a data set, its ROI, segmented AC PC, and u or v coordinates of the detected four landmarks overlaid on the original AC PC plane. Determination of the S Landmark The AC PC slice contains a closed skull which facilitates non-brain tissue removal through the distance criterion. This is not the case for the VPC. We synthesize an image avpc with the closed skull. avpc has two regions: the upper one is the copy from the VPC, while the lower one is the reflection of the upper region with respect to the horizontal line passing through the PC. Figure 2a and b shows the VPC of a data set and the corresponding avpc. The S landmark is localized through segmenting the avpc by the following steps. (1) Find the ROI of the avpc. This procedure is the same as finding the ROI of the AC PC plane. (2) Determine the optimum threshold within the ROI given H l b 20%, H h b 40%. The two percentages are derived from analyzing the ground-truth segmentation of 18 phantom data sets and 20 IBSR data sets (the background proportions largely fall within the range of 25 35%). The threshold maximizing formula (1) is denoted 2. (3) Segment the avpc image through the same substeps as segmenting the AC PC plane, using the optimum threshold 2. (4) Restore object voxels around the object boundaries due to the morphologic opening in a similar way as done for the A, P, L, and R landmarks. (5) Restore object voxels due to the partial volume effect in a similar way as done for the A, P, L, and R landmarks.

4 Extraction of the Modified Talairach Landmarks 973 FIG. 2. Identification of the S landmark through segmenting the derived image avpc (a) original VPC image; (b) derived image avpc with closed skull; (c) segmented avpc image; and (d) horizontal line passing through the extracted S landmark. (6) The v coordinate of the S landmark is the minimum v coordinate of all object voxels in avpc. Figure 2c and d shows the segmented avpc and the original VPC overlaid with a horizontal line indicating the S landmark s v coordinate, respectively. Determination of the I Landmark The I landmark is determined through segmenting the VAC plane and the major challenge is the connection between the brain tissues and noncortical structures due to the optic nerves, which are close to the cortex both spatially and in gray levels in T 1 W and SPGR MR images. In this case it is not possible to synthesize a closed skull image and use the distance criterion; we rather use anatomic knowledge for breaking connections between the cortex and non-brain tissues. In the Talairach Tournoux atlas (1), the z coordinate difference between the AC and the I landmark is 43 mm. We thus assume that the maximum z coordinate difference between the AC and I landmark is within 50 mm (for all the data tested, the maximum difference is 45 mm). This anatomic fact helps to judge whether the noncortical structures have been broken from the cortex around the I landmark in the segmented VAC. The MSP divides the VAC plane into the left and right, roughly anatomically symmetric, halves. The distortion of this symmetry is used for detecting a leakage (connection) between the cortex and non-brain tissues. If it takes place, the other half is used to determine the I landmark. Denote the AC s coordinates in the VAC as (acu1, acv1). The I landmark is obtained as follows. 1. Binarize VAC by assigning all voxels with gray level bigger than the threshold 2 (derived from segmenting the avpc) as foreground voxels and the rest as background voxels. The binarized image is denoted VAC1. 2. Connect the region around the AC: voxels in VAC1 are set to foreground when u acu1 is smaller than 30 mm and v acv1 is smaller than 3 mm (see explanation in step 5). 3. The vertical line passing through the AC divides the VAC into left and right halves. Set the voxels on the vertical line with a v coordinate bigger than (acv1 3) mm to the background so that the foreground is divided into left and right halves in the lower region of VAC1. 4. In VAC1 perform morphologic opening with 3 3SE to get VAC2. 5. In VAC2 perform morphologic erosion with 3 3SE to get VAC3. This helps to further break the connections between the cortex and noncortical structures. Step 2 is to maintain the foreground connectivity after steps 4 and 5 as the neighborhood of AC may have CSF voxels that make the segmented VAC broken around the AC after morphologic opening. 6. Find the connected component from (acu1, acv1) in VAC3 to get the foreground component. Then, perform morphologic dilation on the foreground component with 3 3 SE to get VAC4. Any foreground voxel in VAC4 is considered an object voxel. The erosion followed by finding connected component breaks the connection between the cortex and noncortical structures while preserving the original shape of the cortex. 7. Find the maximum v coordinates of the foreground component in the left (maxvl) and right (maxvr) halves of VAC4. If (maxvl acv1) is bigger than 50 mm, it indicates that the connection between the cortex and the noncortical tissues in the left half of VAC4 is not broken. This rule is also applicable to the right half of VAC4. 8. The left half of VAC4 is processed in two rounds if (maxvl acv1) is smaller than 50 mm. The first round is to compensate for the morphologic opening, i.e., to restore each of eight neighbors of all boundary voxels of VAC4 as an object voxel if its gray level in VAC is bigger than 2. The second round is to compensate for the influence of the partial volume effect done similarly to the S landmark. Similarly, the right half of VAC4 is recovered in two rounds when (maxvr acv1) is smaller than 50 mm. 9. When both (maxvl acv1) and (maxvr acv1) are smaller than 50 mm, the v coordinate of the I landmark is the biggest v coordinate of all object voxels in VAC4. If only one of (maxvl acv1) and (maxvr acv1) is smaller than 50 mm, the v coordinate of the I landmark is the maximum v of object voxels from the half (left or right) whose maximum object v coordinate is smaller than (50 mm acv1). When both (maxvl acv1) and (maxvr acv1) are bigger than 50 mm, the I landmark cannot be located and an error message is issued, as the connection between the cortex and non-brain tissues has not been broken. Figure 3a, b, c, and d shows the original VAC, binarized VAC (VAC1), processed foreground (VAC4), and the v

5 974 Hu et al. Table 2 The Distribution of the Location Errors for All Landmarks 0 1 mm 2 mm 3 mm Number of landmarks Percentage Accuracy Statistics The range, average, and SD of the landmark location errors for the A, P, L, R, I, and S landmarks of all 62 data sets are listed in Table 1. The distribution of errors for all 372 (62 6) landmarks is summarized in Table 2. These errors are: mm (A), mm (P), mm (L), mm (R), mm (I), and mm (S). There are 2 data sets with zero landmark location error, 43 data sets with the maximum location error (MLLE) of 1 mm, 14 data sets with the MLLE of 2 mm, and 3 data sets with the MLLE of 3 mm. Validation Validation is carried out to check the sensitivity of landmark location error to small perturbations of the MSP, AC, and PC, as well as variations in parameters. Sensitivity to MSP, AC, and PC FIG. 3. Identification of the I landmark from processing the VAC image: (a) original VAC image; (b) binarized VAC (VAC1); (c) segmented VAC (VAC4), and (d) horizontal line passing through the extracted I landmark. coordinate of the I landmark overlaid on the original VAC slice. RESULTS The algorithm was implemented in C and tested successfully against 62 diversified MR data sets. The six modified landmarks of each data set were extracted within 2 s (Pentium 4, 2.6 GHz CPU). For each data set, the six ground-truth landmarks were identified on AC PC, VAC, and VPC images by a neuroanatomy expert (WLN) and marked as pixels. Table 1 The Statistics of the Location Errors (in mm) for All Landmarks of 62 Data Sets A P L R I S Range Average SD The three planes defining the six modified Talairach cortical landmarks depend on the accurate extraction of the MSP, AC, and PC. As the MSP (4) can statistically have (0.6, 0.7 mm) error, and AC and PC can deviate up to 1 mm error (based on our recent study (11)), it is necessary to check the sensitivity of the landmarks to these errors. For this purpose, the phantom data with no noise and inhomogeneity is selected. The six modified cortical landmarks are reextracted when this data set s MSP is rotated against the x, y, and z axes by 1 with the AC and PC being the original, and the AC and/or PC s positions deviated from the original by 1 mm with the MSP being the original. The statistics of the six landmarks after perturbation are: mm (A), mm (P), 18 0 mm (R), (L) mm, mm (S), and mm (I), while the positions without perturbation are: 20 mm (A), 197 mm (P), 18 mm (R), 163 mm (L), 26 mm (S), and 161 mm (I). Justification of Parameters The threshold 1 ( 2 ) for binarizing the AC PC image (avpc) is based on the range-constrained thresholding with the upper and lower percentages being 14% (20%) and 28% (40%), respectively. Changing these two percentages to 12% (18%) and 30% (42%) yields little variation in segmentation. When segmenting the AC PC image, morphologic opening with a square SE of 5 mm is used for separation while opening with a smaller square SE of 3 mm is used to maintain the small brain fragments. The combination of these two openings is one of the key procedures to achieve the high accuracy of the landmarks in the AC PC and VPC planes. When segmenting the VAC image, the separation between the brain and non-brain tissues is achieved through erosion, followed by finding the connected component and dilation with a square SE of 3 mm. This sequence separates more efficiently than morphologic opening, followed by finding the connected component (10), while maintaining the shape of the brain. As the closed skull is not available and the distance transform cannot be used to

6 Extraction of the Modified Talairach Landmarks 975 facilitate separation, a small SE is crucial to maintain tiny brain fragments for the high accuracy of the I landmark. Within the region of interest defined by the skull (Fig. 1b), the scalp and bone marrows have a small distance to the background; the empiric 10-mm distance threshold works well to exclude non-brain bright segments for all data sets. DISCUSSION At present, the Talairach transformation is probably the most prevalent method for spatial normalization of neuroimages (3,12,13). It is employed in clinical applications in stereotactic and functional neurosurgery (13), neuroradiology (14), and human brain mapping (3,12) as well as in research, mainly as an initial step applied prior to nonlinear warping (15). Its wide familiarity and acceptance by clinicians is due to its conceptual simplicity and anatomic nature. Nonlinear warping techniques (15), despite their tremendous potential, are not yet accepted in clinical setting due to prohibitory long time (3), black-box nature and difficulty/sensitivity in setting parameters. FIG. 4. Robustness to noise and inhomogeneity of our method as compared with existing methods ((8,9)): (a) original AC PC image of the IBSR data set 6_10 with serious inhomogeneity and noise; (b) segmented based on Ref. (9); (c) segmented based on Ref. (8); and (d) segmented by our algorithm. FIG. 5. The 3-mm location error due to the connection between the sinus and the cortex coupled with the partial volume effect around the P landmark: (left) part of the AC PC image zoomed around the P landmark with the vertical coordinate of the ground-truth P landmark overlaid as a white point; and (right) the horizontal line passing through the extracted P landmark. Although the concept of the Talairach transformation is simple, a robust, accurate, and automatic determination of the Talairach landmarks (either manually or automatically) is a challenging task. Our algorithm achieves that for the modified Talairach cortical landmarks. This algorithm has been validated quantitatively against 62 diversified MR data sets shown to be robust to noise and gray level inhomogeneity (Fig. 4). The average landmark location error is below 1 mm and 94.4% of landmarks have a location error of 0 or 1 mm. High accuracy is achieved through compensation for the connection between the cortex and the sagittal sinus/meninges, noise, gray level inhomogeneity, closeness of the cortex to optic nerves both spatially and in gray levels, and the partial volume effect. The algorithm is fast (less than 2 s on a Pentium 4). The low computational cost results from processing three 2D images and employing only simple operations like thresholding, basic morphologic operations, and distance transform. The algorithm can manage artifacts caused by stereotactic frames and handle different magnetic field strengths (1.5 and 3 T). The algorithm is only minimally sensitive to small perturbations (1 mm, 1 ) of the MSP, AC, and PC. The landmark location accuracy does not necessarily decrease with the decrease in the spatial resolution, as the partial volume effect has been compensated, which is why most of the data sets with 2-mm or even 3.5-mm voxel size still have a landmark location error of 1 mm. The algorithm has several limitations. First, when the sagittal sinus/meninges/optic nerves are mixed with the cortex due to the partial volume effect so that the foreground voxels connecting the brain and non-brain tissues can fill at least a 5 5 square window in the corresponding binarized images completely, these non-brain tissues cannot be excluded from segmentation. Our algorithm is sensitive to this kind of connections, and all 2-mm and 3-mm location errors are due to the undesired connections (Fig. 5). Increasing the size of SE may help break the connection, but bigger brain fragments will be discarded at the same time, which may even deteriorate the landmark location accuracy. Increasing the spatial solution of data seems to be the right solution. Second, when connections between the cortex and non-brain tissues cannot be broken on both halves of the VPC image, the I landmark cannot be localized. This extreme situation has never occurred. Third, when the AC and/or PC cannot be extracted automatically, the cortical landmarks cannot be located. A

7 976 Hu et al. possible option is to manually identify the AC and PC. Fourth, validation has been limited to 62 T 1 W and SPGR MRI data sets. Further study on more data sets (including T 2 -weighted) is needed, despite the current validation effort which included both typical clinical data and complicated cases. We believe that the results obtained demonstrate that our algorithm is a useful component of the automated Talairach transformation enabling automatic scan interpretation (11). REFERENCES 1. Talairach J, Tournoux P. Co-planar Stereotaxic Atlas of the Human Brain. New York: Thieme; Nowinski WL, Thirunavuukarasuu A: The Cerefy Clinical Brain Atlas on CD-ROM. New York: Thieme; Nowinski WL, Thirunavuukarasuu A. Atlas-assisted localization analysis of functional images. Med Image Anal 2001;5: Hu QM, Nowinski WL. A rapid algorithm for robust and automatic extraction of the midsagittal plane of the human cerebrum from neuroimages based on local symmetry and outlier removal. Neuroimage 2003;20: Verard L, Allain P, Travere JM, Baron JC, Bloyet D. Fully automatic identification of AC and PC landmarks on brain MRI using scene analysis. IEEE Trans Med Imaging 1997;16: Cox RW. AFNI. software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 1996;29: Nowinski WL. Modified Talairach landmarks. Acta Neurochir 2001; 143: Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Systems Man Cybernet 1979;9: Cheng HD, Chen J, Li J. Threshold selection based on fuzzy c-partition entropy approach. Pattern Recog 1998;31: Brummer ME, Mersereau RM, Eisner RL, Lewine RRJ. Automatic detection of brain contours in MRI data sets. IEEE Trans Med Imaging 1993;12: Nowinski WL, Hu QM, Bhanu Prakash KN, Qian GY, Thirunavuukarasuu A, Aziz A. Automatic interpretation of normal brain scans. Proc. Radiological Society of North America, 90 Scientific Assembly & Annual Meeting; 28 November 3 December p Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, Fox PT. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapping 2000; 10: Nowinski WL. Computerized brain atlases for surgery of movement disorders. Semin Neurosurg 2001;12: Nowinski WL. Electronic brain atlases: features and applications. In: 3D Image Processing: Techniques and Clinical Applications (Caramella D, Bartolozzi C, editors). Medical Radiology series, New York: Springer-Verlag; 2002: p Toga AW (ed.). Brain Warping. San Diego: Academic Press; 1998.