Fully automated segmentation of corpus callosum in midsagittal brain MRIs.

TitleFully automated segmentation of corpus callosum in midsagittal brain MRIs.
Publication TypeJournal Article
Year of Publication2013
AuthorsLi Y, Mandal M, Ahmed NS
JournalConference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Volume2013
Pagination5111-4
Date Published2013
Abstract

In the diagnosis of various brain disorders by analyzing the brain magnetic resonance images (MRI), the segmentation of corpus callosum (CC) is a crucial step. In this paper, we propose a fully automated technique for CC segmentation in the T1-weighted midsagittal brain MRIs. An adaptive mean shift clustering technique is first used to cluster homogenous regions in the image. In order to distinguish the CC from other brain tissues, area analysis, template matching, in conjunction with the shape and location analysis are proposed to identify the CC area. The boundary of detected CC area is then used as the initial contour in the Geometric Active Contour (GAC) model, and evolved to get the final segmentation result. Experimental results demonstrate that the proposed technique overcomes the problem of manual initialization in existing GAC technique, and provides a reliable segmentation performance.

DOI10.1109/EMBC.2013.6611204
Alternate JournalConf Proc IEEE Eng Med Biol Soc