A comparison of three preprocessing techniques for kidney stone segmentation in CT scan images

Accurate segmentation techniques used in the automated kidney stone detection is one of the most challenging problems because of grey levels similarities of adjacent organs, variation in shapes and positions of kidney stone. Valuable image preprocessing is an essential step to improve the performance of region of interest (ROI) segmentation by removing unwanted region (non ROI), noise and disturbance. The research aims to conduct comparative study of the three different preprocessing techniques for the noise removal from the CT image of kidney stone. Three noise removal techniques are computed based on the size-based thresholding (method I), shape-based thresholding(method II) and hybrid thresholding algorithm (method III). T he methods aim to enhance their readability and to assist the segmentation process in the kidney stone diagnosis system. Digitized transverse abdomen CT images from 75 patients with kidney stone cases were done in statistical analysis and validation. The estimation of coordinate points in the stone region was measured independently by the expert radiologists to get the validation data for the analysis. The results show that the proposed method I, II and III have a sensitivity of 90.91%, 92.93% and 68.69%, respectively. The execution times of overall process were 9.44 sec, 10.14 sec and 34.5 in average, respectively.