The possibilities of improvement in the sensitivity of cancer fluorescence diagnostics by computer image processing

Aleksandra Ledwon M.D.; Robert Bieda; Aleksandra Kawczyk-Krupka; Andrzej Polanski; Konrad Wojciechowski; Wojciech Latos; Karolina Sieron-Stoltny; Aleksander Sieron M.D.

doi:10.1117/12.762323

5 March 2008 The possibilities of improvement in the sensitivity of cancer fluorescence diagnostics by computer image processing

Aleksandra Ledwon M.D., Robert Bieda, Aleksandra Kawczyk-Krupka, Andrzej Polanski, Konrad Wojciechowski, Wojciech Latos, Karolina Sieron-Stoltny, Aleksander Sieron M.D.

Author Affiliations +

Proceedings Volume 6859, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues VI; 68591E (2008) https://doi.org/10.1117/12.762323
Event: SPIE BiOS, 2008, San Jose, California, United States

Abstract

Background: Fluorescence diagnostics uses the ability of tissues to fluoresce after exposition to a specific wavelength of light. The change in fluorescence between normal and progression to cancer allows to see early cancer and precancerous lesions often missed by white light. Aim: To improve by computer image processing the sensitivity of fluorescence images obtained during examination of skin, oral cavity, vulva and cervix lesions, during endoscopy, cystoscopy and bronchoscopy using Xillix ONCOLIFE. Methods: Function of image f(x,y):R² → R³ was transformed from original color space RGB to space in which vector of 46 values refers to every point labeled by defined xy-coordinates- f(x,y):R² → R⁴⁶. By means of Fisher discriminator vector of attributes of concrete point analalyzed in the image was reduced according to two defined classes defined as pathologic areas (foreground) and healthy areas (background). As a result the highest four fisher's coefficients allowing the greatest separation between points of pathologic (foreground) and healthy (background) areas were chosen. In this way new function f(x,y):R² → R⁴ was created in which point x,y corresponds with vector Y, H, a*, c₂. In the second step using Gaussian Mixtures and Expectation-Maximisation appropriate classificator was constructed. This classificator enables determination of probability that the selected pixel of analyzed image is a pathologically changed point (foreground) or healthy one (background). Obtained map of probability distribution was presented by means of pseudocolors. Results: Image processing techniques improve the sensitivity, quality and sharpness of original fluorescence images. Conclusion: Computer image processing enables better visualization of suspected areas examined by means of fluorescence diagnostics.

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Aleksandra Ledwon M.D., Robert Bieda, Aleksandra Kawczyk-Krupka, Andrzej Polanski, Konrad Wojciechowski, Wojciech Latos, Karolina Sieron-Stoltny, and Aleksander Sieron M.D. "The possibilities of improvement in the sensitivity of cancer fluorescence diagnostics by computer image processing", Proc. SPIE 6859, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues VI, 68591E (5 March 2008); https://doi.org/10.1117/12.762323

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