###### Abstract
In 1981, it was extended by [PERSON] into the Generalized HT (GHT), which is a two-phase learning-detection process to detect arbitrary complex non-parametric shapes [14]. Continuously, different variations of Hough transform have been proposed by many researchers, like probabilistic Hough transform [15], randomized Hough transform [16], and Vector-Gradient Hough Transform (VGHT) [17]. In 1994, [PERSON], [PERSON], and [PERSON] introduced the fuzzy Hough transform [18] to detect fuzzy lines and circles in the noisy environments by roughly fitting the data points to given parametric forms. Hough transform and all its successive versions have proven to be typical powerful techniques with promising outcomes in numerous fields of applications, for example but not limited to, object detection [19; 20], lanes and roads detection [21; 22], industrial automation [23], mechanical engineering [24], medical image processing [25; 26; 27; 28; 29], and robot navigation [30] fields. Drawing from our experience and a comprehensive analysis of previous studies employing the Hough transform, we briefly outline the practical strengths and weaknesses of this method in Table 1.  
The research community has investigated the limitations of Hough transform and suggested different approaches to make it a more plausible tool. To solve large calculation demand and to ensure integration with the Wireless Capsule Endoscopy (WCE) system, authors in [31] improved the real-time computation of the Hough transform. The design of the new approach took into consideration specific constraints of WCE such as limited space and limited energy resources. Within the same limitation direction (i.e., minimizing the Hough transform's computational cost), an Edge Orientation-based Fuzzy Hough Transform (EOFHT) was proposed in [32]. Instead of using all the edge-detected image points in the voting process, just those specific points whose representation is consistent with the selected gradient orientation range were eligible to vote. Moreover, since shorter curves give fewer votes, thus, circles with smaller radii give weaker peaks in the accumulator space, a weighted vote which is inversely proportional to the radius in the parameter space was given to each entitled point in the image space by the modified voting method in [33]. Currently, automatic colorectal polyp detection and localization in colonoscopy images is a promising research area and a challenging problem because of the high variability of the colorectal polyps' characteristics in both shape and texture. Thus, the efficiency of applying Hough transform was also studied in this field. Classical Hough transform was applied in [34] to identify potential regions of interest (ROIs) in 300 video endoscopy pictures. A good detection of the ROIs containing a polyp was possible using classical Hough transform based on Canny edge detection approach. However, in several samples the method generated many alternative weaker circles, and raised the classification system's False Positive Rate (FPR). To enhance the effectiveness of the proposed method, after Hough transform step, the textural characteristics from co-occurrence matrices were computed, and then used within a boosting-based technique for the final classification purpose. Hough transform was also used in other colorectal polyp localizing methods [35; 36] as a ready-made preprocessing step to find ROIs. However, they concentrated on the steps determining whether a ROI contains polyp or not, and not on the Hough transform step itself. (Linear Hough transform was also used in colonoscopy for detecting folds within the bowel [37].) In our research, instead of improving the steps after selecting the ROIs with Hough transform (like in [34; 35; 36]), we would like to overcome some weaknesses of the Hough transform itself. The circles arising from the fuzzy Hough transform can also serve as initial masks for active contour methods [38; 39].