Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model

Elsa D. Angelini; Ting Song; Brett D. Mensh; Andrew Laine

doi:10.1117/12.535860

12 May 2004 Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model

Elsa D. Angelini, Ting Song, Brett D. Mensh, Andrew Laine

Proceedings Volume 5370, Medical Imaging 2004: Image Processing; (2004) https://doi.org/10.1117/12.535860
Event: Medical Imaging 2004, 2004, San Diego, California, United States

Abstract

Segmentation of three-dimensional anatomical brain images into tissue classes has applications in both clinical and research settings. This paper presents the implementation and quantitative evaluation of a four-phase three-dimensional active contour implemented with a level set framework for automated segmentation of brain MRIs. The segmentation algorithm performs an optimal partitioning of three-dimensional data based on homogeneity measures that naturally evolves to the extraction of different tissue types in the brain. Random seed initialization was used to speed up numerical computation and avoid the need for a priori information. This random initialization ensures robustness of the method to variation of user expertise, biased a priori information and errors in input information that could be influenced by variations in image quality. Experimentation on three MRI brain data sets showed that an optimal partitioning successfully labeled regions that accurately identified white matter, gray matter and cerebrospinal fluid in the ventricles. Quantitative evaluation of the segmentation was performed with comparison to manually labeled data and computed false positive and false negative assignments of voxels for the three organs. We report high accuracy for the two comparison cases. These results demonstrate the efficiency and flexibility of this segmentation framework to perform the challenging task of automatically extracting brain tissue volume contours.

Citation Download Citation

Elsa D. Angelini, Ting Song, Brett D. Mensh, and Andrew Laine "Segmentation and quantitative evaluation of brain MRI data with a multiphase 3D implicit deformable model", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.535860

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