Department of Electrical & Computer Engineering Signal and Image Laboratory (SaIL) The University of Arizona®

Past Research

Automated Segmentation & Classification of Medical Images

Students: Te-shen "Dickson" Liang, James L. Lee, Jesse C. Ma

We have developed application-specific techniques for the automated segmentation of magnetic resonance images of the head. Segmentation involves the labeling of homogenous regions in the 3-D image data set. After segmentation, each voxel is classified according to tissue type: white matter, gray matter, cerebral spinal fluid, etc. Once the 3-D data set has been segmented and classified, we apply computer graphics methods to generate a rendered, 3-D view of the surface of the brain. We have investigated several approaches to the segmentation/classification problem, including 3-D edge detection, fuzzy c-means, neural networks, and the watershed transformation. Applications of this work include visualization of data for surgical planning, visual support for diagnosis of neurological disorders and tumors, and metrological studies in neuroscience.

Publications:

  1. Te-shen Liang and Jeffrey J. Rodriguez, "MR Cranial Image Segmentation A Morphological and Clustering Approach," in Proc. IEEE Southwest Symp. on Image Analysis and Interpretation, San Antonio, Texas, April 8-9, 1996, pp. 184-189. [ PDF ]

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