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A novel learning based segmentation method for rodent brain structures using MRI

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9 Author(s)
Jinghao Zhou ; CBIM, Rutgers, The State University of New Jersey, Piscataway, U.S.A. ; Sukmoon Chang ; Qingshan Liu ; George Pappas
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This paper reports a novel method for fully automated segmentation of rodent brain volume by extending the robust active shape models to incorporate an automatic prior shape selection process. This automatic prior shape selection process using support vector machines provides an automatic shape initialization method for further segmentation of rodent brain structures such as Cerebellum, Neocortex, Corpus Callosum, External Capsule, Caudate Putamen, Hippocampus and Ventricles with the robust active shape model framework in magnetic resonance images (MRI). The mean successful rate of this classification method shows 92.2% accuracy compared to the expert-defined ground truth. We also demonstrate the very promising segmentation results of the robust active shape model framework in rodent brain volume.

Published in:

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

14-17 May 2008