Segmentation of volumetric medical imagery using multiple geodesic-based active surfaces

16 April 1996 Segmentation of volumetric medical imagery using multiple geodesic-based active surfaces

David J. Schlesinger, John W. Snell, Lois E. Mansfield, James R. Brookeman, James M. Ortega, Neal F. Kassell M.D.

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Proceedings Volume 2710, Medical Imaging 1996: Image Processing; (1996) https://doi.org/10.1117/12.237927
Event: Medical Imaging 1996, 1996, Newport Beach, CA, United States

Abstract

Segmentation of volumetric medical imagery often benefits from the incorporation of a-priori knowledge of anatomical structure into the problem domain. Our previous work involved the use of active surfaces based on spherical mappings of rectangular grids to represent the anatomical structure of the object of interest. We have developed an improved method using active surfaces based on multiple geodesics which can dynamically link together to form object models. Deformable geodesic models are topologically closed structures, making them a better approximation of the types of objects found in volumetric imagery. In addition, geodesic surfaces exhibit regular sampling intervals which facilitate collision detection between different surfaces, allowing constraints among different surfaces to be included in the segmentation scheme. In this paper we present an overview of our current active surface segmentation system. Initial results of the system as applied to the problem of the segmentation of the major sub-parts of the brain are also presented. The results show favorable potential for the use of geodesic-based surfaces as a means for the segmentation of closed objects from volumetric medical imagery.

Citation Download Citation

David J. Schlesinger, John W. Snell, Lois E. Mansfield, James R. Brookeman, James M. Ortega, and Neal F. Kassell M.D. "Segmentation of volumetric medical imagery using multiple geodesic-based active surfaces", Proc. SPIE 2710, Medical Imaging 1996: Image Processing, (16 April 1996); https://doi.org/10.1117/12.237927

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