PC-based entropy measures for analyzing leakage from microvessels

Ahmed H. Desoky; Steven Hall; Patrick D. Harris; Khaled A. Kamel; Carol O'Connor

doi:10.1117/12.18908

1 July 1990 PC-based entropy measures for analyzing leakage from microvessels

Ahmed H. Desoky, Steven Hall, Patrick D. Harris, Khaled A. Kamel, Carol O'Connor

Proceedings Volume 1233, Medical Imaging IV: Image Processing; (1990) https://doi.org/10.1117/12.18908
Event: Medical Imaging '90, 1990, Newport Beach, CA, United States

Abstract

Based on the assertion that entropy and leakage are related this paper describes the software implementation of a texture analysis technique which is based on entropy for analyzing macromolecular leakage from microvessels. Images of vessel leakage were compared to a pre-leakage image by computing their percent changes in entropy to give a relative measure of leakage. Entropy calculations were tested on different region sizes of the images to determine the regional sources as well as topographical spread of the leakage. Since entropy can be based on the statistics of both gray level components and frequency components the FWT (Fast-Walsh Transform) FF1'' (Fast-Fourier Transform) DCT (Discrete-Cosine Transform) and histogram routines were implemented in C to investigate the effects of transform type on the entropy measure. The percent changes in entropy from the frequency analyses were found to be more significant ''than changes in entropy from the histogram approach. Moreover the FWT was found to be comparable to the FFT and DCT with regard to the entropy measure and was thus chosen as the better transformation because it decreased computation time and memory requirements. This software package successfully produced a texture analysis technique based on entropy. However the exact quantitative relationship between vessel leakage and entropy measures has not fully been established. . 1.

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Ahmed H. Desoky, Steven Hall, Patrick D. Harris, Khaled A. Kamel, and Carol O'Connor "PC-based entropy measures for analyzing leakage from microvessels", Proc. SPIE 1233, Medical Imaging IV: Image Processing, (1 July 1990); https://doi.org/10.1117/12.18908

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KEYWORDS

Fast wavelet transforms

Image processing

Analytical research

Image analysis

Medical imaging

Software development

Statistical analysis

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