Cortex segmentation: a fast variational geometric approach

• Published in: IEEE transactions on medical imaging Vol. 21; no. 12; pp. 1544 - 1551
• Main Authors: Goldenberg, R., Kimmel, R., Rivlin, E., Rudzsky, M.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-12-2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active contours; Algorithms; Anatomy, Cross-Sectional - methods; Biological and medical sciences; Brain modeling; Cerebral Cortex - anatomy & histology; Cerebral Cortex - diagnostic imaging; Cities and towns; Computer science; Computer Simulation; Databases, Factual; Deformable models; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image segmentation; Imaging, Three-Dimensional - methods; Magnetic resonance; Magnetic Resonance Imaging - methods; Medical sciences; Pattern Recognition, Automated; Reproducibility of Results; Rough surfaces; Sensitivity and Specificity; Surface reconstruction; Surface roughness; Tomography, X-Ray Computed - methods
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2002.806594

An automatic cortical gray matter segmentation from a three-dimensional (3-D) brain images [magnetic resonance (MR) or computed tomography] is a well known problem in medical image processing. In this paper, we first formulate it as a geometric variational problem for propagation of two coupled bounding surfaces. An efficient numerical scheme is then used to implement the geodesic active surface model. Experimental results of cortex segmentation on real 3-D MR data are provided.