Texture analysis and tissue segmentation of cryosection images

Tamara S. Williams; Jennifer L. Casper

doi:10.1117/12.348500

21 May 1999 Texture analysis and tissue segmentation of cryosection images

Tamara S. Williams, Jennifer L. Casper

Author Affiliations +

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348500
Event: Medical Imaging '99, 1999, San Diego, CA, United States

Abstract

This paper outlines the exploration of two methods to detect texture in a digital cryosection image from the Visible Human Project. For the purpose of this research, texture is defined as a regular or irregular placement of color in an image. A higher-level decision-making algorithm was employed to extract different body tissues: fat, muscle, and bone. This algorithm was designed on the premise that each body tissue has a different visible texture. Another method utilized an artificial intelligence approach, a neural net, to extract textured tissues. Each problem demands a unique neural net; hence, this neural net is customized in terms of the image dataset and the goal of texture detection.

Citation Download Citation

Tamara S. Williams and Jennifer L. Casper "Texture analysis and tissue segmentation of cryosection images", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348500

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