AUTOMATED CORPUS CALLOSUM SEGMENTATION IN MIDSAGITTAL BRAIN MR IMAGES

• Main Authors: Yue Li, Huiquan Wang, Nizam Ahmed, Mrinal Mandal
• Format: Article
• Language: English
• Published: ICT Academy of Tamil Nadu 2017-08-01
• Series: ICTACT Journal on Image and Video Processing
• Subjects: Adaptive Mean Shift Clustering; Automated Segmentation; Corpus Callosum; Geometric Active Contour; Template Matching
• Online Access: http://ictactjournals.in/ArticleDetails.aspx?id=3124
• ISSN: 0976-9099; 0976-9102

Corpus Callosum (CC) is an important white-matter structure in the human brain. Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high resolution images for the structures. Segmentation is an important step in medical image analysis. This paper proposes a fully automated technique for segmentation of CC on the midsagittal slice of T1-weighted brain MR images. The proposed technique consists of three modules. First it clusters all homogenous regions in the image with an adaptive mean shift (AMS) technique. The automatic CC contour initialization (ACI) is achieved using the region analysis, template matching and location analysis, thus identify the CC region. Finally, the boundary of recognized CC region is used as the initial contour in the Geometric Active Contour (GAC) model, and is evolved to obtain the final segmentation result of CC. Experimental results demonstrate that the proposed AMS-ACI technique is able to provide accurate initial CC contour, and the proposed AMS-ACI-GAC technique overcomes the problem of user-guided initialization in existing GAC techniques, and provides a reliable and accurate performance in CC segmentation.