ICTACT Journal on Image and Video Processing

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Automated Corpus Callosum Segmentation in Midsagittal Brain MR Images


Affiliations
1 Department of Electrical and Computer Engineering, University of Alberta, Canada
2 Department of Medicine, University of Alberta, Canada
     

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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.

Keywords

Adaptive Mean Shift Clustering, Automated Segmentation, Corpus Callosum, Geometric Active Contour, Template Matching.
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  • Automated Corpus Callosum Segmentation in Midsagittal Brain MR Images

Abstract Views: 311  |  PDF Views: 7

Authors

Yue Li
Department of Electrical and Computer Engineering, University of Alberta, Canada
Huiquan Wang
Department of Electrical and Computer Engineering, University of Alberta, Canada
Nizam Ahmed
Department of Medicine, University of Alberta, Canada
Mrinal Mandal
Department of Electrical and Computer Engineering, University of Alberta, Canada

Abstract


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.

Keywords


Adaptive Mean Shift Clustering, Automated Segmentation, Corpus Callosum, Geometric Active Contour, Template Matching.

References