Abstract
In this paper, a computer assistive tool is proposed to Process and analyse ultrasound Kidney Images for the classification of Renal Pathologies. The Ultrasound Kidney Images are classified into four classes: Normal, Cyst, Calculi and Tumor. Scanned Kidney Ultra-Sound (US) Images are obtained and Knowledge pertaining to common Pathologies from an Urologist Perspective is utilized as inputs to carry out the classification. The Images are preprocessed for the removal of Speckle noises by applying Median and Gaussian filter. Optimal thresholding segmentation algorithm is used to obtain the region of Interest. A set of first order statistical features are extracted. These features are given as inputs for training and testing the probabilistic neural network classifier. Hold out method is adopted where in 50% images are used for training and remaining 50% images are used for testing. The efficiency of the classifier is finally evaluated. A classification rate of 93.5% is obtained. The results achieved, are based on performance metrics calculations and are highly satisfactory.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
