DEVELOPMENT OF AUTOMATED SEGMENTATION TOOL FOR MOUSE BRAIN

Duke University, Durham NC

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Abstract

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. MR microscopy - shared MR mouse brain data with the investigator. Develop methodology for automated segmentation of the mouse brain into neuroanatomical structures. In specifically, we are planning to explore the applicability of Support Vector Machine (SVM) and Markov Random Field (MRF) for mouse brain segmentation. SVMs have been applied to numerous fields including classification of brain PET images, optical character recognition, object detection, face verification, text categorization and so on. To our knowledge, applying SVM to classify mouse brain structures has been less touched. Recently literature indicates Ali et al (2005) explored the use of Markov Random Field (MRF) for mouse brain segmentation. Given smaller sample size of subjects, SVM tends to have superior results than techniques such as MRF. Therefore, we would like to first conduct comparison study. We will compare the segmentation results of SVM with that of MRF.