Improved Detection of Kidney Stone in Ultrasound Images Using Segmentation Techniques

The medical images are often corrupted by various noises and blurriness. In particular, the noises presented in ultrasound images may lead to an inaccurate diagnosis of smaller kidney stones and affect its treatment. This paper proposes an improved technique for detection of kidney stone from the ultrasound images of kidney. The ultrasound kidney images are preprocessed to remove labels and change from RGB to Gray images. Further, image contrast is enhanced by adjusting the image intensity. To remove the noises, median filtering is used. The filtered image is taken as input for morphological segmentation process; initially applied dilation and then seed region growing algorithm is used to segment the renal calculi from ultrasound image of kidney. The region parameters are extracted from the segmented region. Finally, area of each renal calculi is calculated. The various performance evaluation GLCM features such as entropy, contrast, angular second moment and correlation are used to judge the quality of output images. The confusion matrix is also prepared to analyze the sensitivity, specificity, and accuracy of the final system. The overall accuracy of classification system is around 90%. The proposed technique may help medical professionals in easy detection of kidney stones and benefit the patients.