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Fully Automatic Segmentation of MRI Brain Images using Probabilistic Anisotropic Diffusion and Multi-Scale Watersheds
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.ORCID iD: 0000-0002-9081-2170
2003 (English)In: Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision, Springer Berlin/Heidelberg, 2003, Vol. 2695, 641-656 p.Conference paper (Other academic)
Abstract [en]

This article presents a fully automatic method for segmenting the brain from other tissue in a 3-D MR image of the human head. The method is a an extension and combination of previous techniques, and consists of the following processing steps: (i) After an initial intensity normalization, an affine alignment is performed to a standard anatomical space, where the unsegmented image can be compared to a segmented standard brain. (ii) Probabilistic diffusion, guided by probability measures between white matter, grey matter and cerebrospinal fluid, is performed in order to suppress the influence of extra-cerebral tissue. (iii) A multi-scale watershed segmentation step creates a slightly over-segmented image, where the brain contour constitutes a subset of the watershed boundaries. (iv) A segmentation of the over-segmented brain is then selected by using spatial information from the pre-segmented standard brain in combination with additional stages of probabilistic diffusion, morphological operations and thresholding. The composed algorithm has been evaluated on 50 T1-weighted MR volumes, by visual inspection and by computing quantitative measures of (i) the similarity between the segmented brain and a manual segmentation of the same brain, and (ii) the ratio of the volumetric difference between automatically and manually segmented brains relative to the volume of the manually segmented brain. The mean value of the similarity index was 0.9961 with standard deviation 0.0034 (worst value 0.9813, best 0.9998). The mean percentage volume error was 0.77 % with standard deviation 0.69 % (maximum percentage error 3.81 %, minimum percentage error 0.05 %).

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2003. Vol. 2695, 641-656 p.
, Lecture Notes in Computer Science, 2695
National Category
Computer Vision and Robotics (Autonomous Systems) Bioinformatics (Computational Biology)
URN: urn:nbn:se:kth:diva-40154DOI: 10.1007/3-540-44935-3_45OAI: diva2:451266
Scale-Space Methods in Computer Vision

QC 20111025

Available from: 2013-04-22 Created: 2011-09-13 Last updated: 2013-04-22Bibliographically approved

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