Extracting line representations of sulcal and gyral patterns in MR images of the human brain

This paper describes automatic procedures for extracting sulcal and gyral patterns from magnetic resonance (MR) images of the human brain. Specifically, the authors present three algorithms for the extraction of gyri, sulci, and sulcal fundi. These algorithms yield highly condensed line representations which can be used to describe the individual properties of the neocortical surface. The algorithms consist of a sequence of image analysis steps applied directly to the volumetric image data without requiring intermediate data representations such as surfaces or three-dimensional renderings. Previous studies have mostly focused on the extraction of surface representations, rather than line representations of cortical structures. The authors believe that line representations provide a valuable alternative to surface representations.

[1]  A. Toga,et al.  Detection and mapping of abnormal brain structure with a probabilistic atlas of cortical surfaces. , 1997, Journal of computer assisted tomography.

[2]  Richard M. Leahy,et al.  Surface-based labeling of cortical anatomy using a deformable atlas , 1997, IEEE Transactions on Medical Imaging.

[3]  King-Sun Fu,et al.  A parallel thinning algorithm for 3-D pictures , 1981 .

[4]  A. Toga,et al.  Three-Dimensional Statistical Analysis of Sulcal Variability in the Human Brain , 1996, The Journal of Neuroscience.

[5]  Gilles Bertrand,et al.  A Boolean characterization of three-dimensional simple points , 1996, Pattern Recognition Letters.

[6]  Gabriele Lohmann,et al.  Automatical Adaption of the Stereotactical Coordinate System in Brain MRI Datasets , 1997, IPMI.

[7]  Jean-Francois Mangin,et al.  Differential features of cortical folds , 1997, CVRMed.

[8]  R. Kikinis,et al.  Characterization and recognition of 3D organ shape in medical image analysis using skeletonization , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[9]  Markus Ilg,et al.  Voronoi skeletons: theory and applications , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Nicholas Ayache,et al.  Smoothing and matching of 3-d space curves , 1992, Other Conferences.

[11]  山浦 晶.  Atlas of the Cerebral Sulci, Michio Ono, Stefan Kubik and Chad D. Abernathey著, Georg Thieme Verlag, Stuttgart, New York 1990(らいぶらりい) , 1992 .

[12]  J. Régis,et al.  Generic model for the localization of the cerebral cortex and preoperative multimodal integration in epilepsy surgery. , 1995, Stereotactic and functional neurosurgery.

[13]  Gábor Székely,et al.  Estimating shortest paths and minimal distances on digitized three-dimensional surfaces , 1993, Pattern Recognit..

[14]  Christos Davatzikos,et al.  Mapping the Cerebral Sulci: Application to Morphological Analysis of the Cortex and to Non-rigid Registration , 1997, IPMI.

[15]  Gabriele Lohmann,et al.  Automatic Detection of Sulcal Bottom Lines in MR Images of the Human Brain , 1997, IPMI.

[16]  Rangasami L. Kashyap,et al.  Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms , 1994, CVGIP Graph. Model. Image Process..

[17]  R. Schafer,et al.  Morphological systems for multidimensional signal processing , 1990, Proc. IEEE.

[18]  Ben J. H. Verwer,et al.  Local distances for distance transformations in two and three dimensions , 1991, Pattern Recognit. Lett..

[19]  G. Borgefors.  Distance transformations in arbitrary dimensions , 1984 .

[20]  Gunilla Borgefors,et al.  On Digital Distance Transforms in Three Dimensions , 1996, Comput. Vis. Image Underst..

[21]  Frithjof Kruggel.  Automatical Adaptation of Anatomical Masks to the Neocortex , 1995, CVRMed.

[22]  Nicholas Ayache,et al.  Automatic Retrieval of Anatomical Structures in 3D Medical Images , 1995, CVRMed.

[23]  P. Danielsson.  Euclidean distance mapping , 1980 .

[24]  Gilles Bertrand,et al.  A new characterization of three-dimensional simple points , 1994, Pattern Recognition Letters.

[25]  Christos Davatzikos,et al.  Finding 3 D Parametric Representations of the Deep Cortical FoldsMarc Vaillant , 1996 .

[26]  Nicholas Ayache,et al.  Topological segmentation of discrete surfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[27]  Azriel Rosenfeld,et al.  Digital topology: Introduction and survey , 1989, Comput. Vis. Graph. Image Process..

[28]  Jun-ichiro Toriwaki,et al.  New algorithms for euclidean distance transformation of an n-dimensional digitized picture with applications , 1994, Pattern Recognit..

[29]  R. Kikinis,et al.  Temporal lobe sulco-gyral pattern anomalies in schizophrenia: an in vivo MR three-dimensional surface rendering study , 1994, Neuroscience Letters.

[30]  Gabriele Lohmann,et al.  Brain (Brain image analysis) - A toolkit for the analysis of multimodal brain datasets , 1996 .

[31]  Alan C. Evans,et al.  Human cingulate and paracingulate sulci: pattern, variability, asymmetry, and probabilistic map. , 1996, Cerebral cortex.

[32]  Nuggehally Sampath Jayant,et al.  An adaptive clustering algorithm for image segmentation , 1989, International Conference on Acoustics, Speech, and Signal Processing,.