Region of interest detection in ultrasound image is a challenging task due to heterogeneous texture and presence of speckle noise. The ultrasound scanning is most frequently used tool to examine the patient for abnormities, especially presence of stone, in the kidney. Automatic object detection in ultrasound images is burning research areas and the present research work is in the same direction. We have developed an application, which helps the medical practitioner to identify the stone region in the ultrasound image. It is a semiautomatic system in which practitioner need to select the region, which is analyzed, by the proposed system for presence of stone. The feature extraction is applied on cropped regions, which may contain stone. The various features such as Contrast, Angular second moment, Entropy and Correlation are used. The KNN classifier is used to classification based on training image dataset. The overall accuracy of classification system is around 91%. The confusion matrix is also prepared to analyze the complexity and accuracy of the proposed system.