Quantitative Study of Brain Anatomy

Mei Chen, Takeo Kanade, Henry Rowley, and Dean Pomerleau
June, 1998.


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Abstract
We introduce a system that automatically segments and classifies features in brain MRIs. It takes 22 minutes to segment 144 structures in a 256x256x124 voxel image on an SGI computer with three 194 MHz R10K processors. The accuracy is comparable to manual segmentation, which can take an expert at least 8 months. The process starts with an atlas, a hand segmented and classified MRI of a normal brain. Given a subject's data, the atlas is warped in 3-D using a hierarchical deformable matching algorithm until it closely matches the subject, i.e. the atlas is customized for the subject. The customized atlas contains the segmentation and classification of the subject's anatomical structures. Qualitative and quantitative evaluations of the system's performance show promise for applications in the quantitative study of brain anatomy. We have obtained initial results for finding the normal range of variation in the size and symmetry properties of anatomical structures, and for detecting pathologies.

Notes
Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Project(s): Knowledge-Guided Deformable Registration

Text Reference
Mei Chen, Takeo Kanade, Henry Rowley, and Dean Pomerleau, "Quantitative Study of Brain Anatomy," June, 1998.

BibTeX Reference
@inproceedings{Chen_1998_2601,
   author = "Mei Chen and Takeo Kanade and Henry Rowley and Dean Pomerleau",
   title = "Quantitative Study of Brain Anatomy",
   booktitle = "",
   publisher = "IEEE Workshop on Biomedical Image Analysis",
   month = "June",
   year = "1998",
}