Regional Kidney Stone Detection and Classification In Ultrasound Images

Abstract

The ultrasound region of interest is a challenge since the textures and noises are diverse. The most common technique is the ultrasound scans to check for abnormalities in the kidney, in particular the presence of stones. Automatic ultrasonic object detection burns research fields and the study effort now under way is along the same line. Application that allows the physician to detect the stone area in the ultrasound picture has been designed. The practitioner needs to pick the location that is evaluated by the suggested stone presence system. The extraction feature is used to areas that might contain stone. Different characteristics, such contrast, second angular moment, entropy and correlation, are employed. The KNN classification is used for training picture dataset categorization. Classification system total accuracy is about 91%. The confusion matrix will also assess the complexity and exactness of the system being suggested.