The effect of background and outlier subtraction on the structural entropy of two-dimensional measured data Online publication date: Fri, 18-Sep-2020
by Szilvia Nagy; Brigita Sziová; Levente Solecki
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 12, No. 3, 2020
Abstract: For colonoscopy images the main information is in the fine structure of the surface of the bowel or colorectal polyps, similarly to the case of combustion engine cylinder surface scans, where the grooving and wear can be detected from the fine pattern superposed to a cylinder curvature. In both cases appear outliers, colonoscopy images have many reflections, whereas the roughness scanners detect small dust particles as well as the micron scale vibrations from the environment. The method presented in this paper takes care of both the problems using histogram stretching together with a special type of filtering. Also, masks are introduced in order to control the effect of the operators. The effects of the processing steps on the structural entropy of the image is also studied, as structural entropies are used in characterisation of the images. By removing the background makes the structural entropies much smaller, and by suppressing the outliers the structural entropies increase.
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