Hybrid Segmentation Framework for Tissue Images Containing Gene Expression Data

Associating speci c gene activity with speci c functional locations in the brain anatomy results in a greater understanding of the role of the gene's products. To perform such an association for the over 20,000 or so genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this work, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quanti ed and compared with other images. Our method utilizes models of shape of training images, texture differentiation at region boundaries, and features of anatomical landmarks to deform a subdivision mesh-based atlas to t gene expression images. The subdivision mesh provides a common coordinate system for internal brain data through which gene expression patterns can be compared across images. The automated large-scale annotation...

• Type: Conference
• Year: 2005
• Where: MICCAI
• Authors: Musodiq Bello, Tao Ju, Joe D. Warren, James Carson, Wah Chiu, Christina Thaller, Gregor Eichele, Ioannis A. Kakadiaris
• Keywords: Gene Expression Images, Gene Expression Patterns, Medical Imaging, MICCAI 2005, Scientists Interpret Gene