A Novel Gradient-Weighted Voting Approach for Classical and Fuzzy Circular Hough Transforms and Their Application in Medical Image Analysis—Case Study: Colonoscopy

Abstract

Classical circular Hough transform was proven to be effective for some types of colorectal polyps. However, the polyps are very rarely perfectly circular, so some tolerance is needed, that can be ensured by applying fuzzy Hough transform instead of the classical one. In addition, the edge detection method, which is used as a preprocessing step of the Hough transforms, was changed from the generally used Canny method to Prewitt that detects fewer edge points outside of the polyp contours and also a smaller number of points to be transformed based on statistical data from three colonoscopy databases. According to the statistical study we performed, in the colonoscopy images the polyp contours usually belong to gradient domain of neither too large, nor too small gradients, though they can also have stronger or weaker segments. In order to prioritize the gradient domain typical for the polyps, a relative gradient-based thresholding as well as a gradient-weighted voting was introduced in this paper. For evaluating the improvement of the shape deviation tolerance of the classical and fuzzy Hough transforms, the maximum radial displacement and the average radius were used to characterize the roundness of the objects to be detected. The gradient thresholding proved to decrease the calculation time to less than 50% of the full Hough transforms, and the number of the resulting circles outside the polyp’s environment also decreased, especially for low resolution images.