Indian Journal of Science and Technology
Year: 2015, Volume: 8, Issue: 24, Pages: 1-5
T. Mangayarkarasi1 * and D. Najumnissa Jamal2
1 Department of Instrumentation and Control Engineering, Sri Sai Ram Engineering College, B.S. Abdur Rahman University, Chennai – 600048, Tamil Nadu, India; [email protected]
2 Department of Instrumentation and Control Engineering, B.S. Abdur Rahman University, Chennai – 600048, Tamil Nadu, India; [email protected]
Ultrasound Scan is the most popular and cheapest imaging technique which is used as a preliminary investigating tool by the doctors nowadays. In this regard, development of a computer aided assistive tool is utmost necessary to create a user friendly interface for the patients to expose them to basic treatment procedures available in case of abnormalities found in the Ultrasound Scan. An easy and better understanding of the impressions given in the scan is vital for further treatment procedures. As a first step towards creating a complete computer aided treatment assistive tool, the abnormalities detection in kidney ultrasound images is taken up as a primary goal in this paper. The most common renal problems that can be identified in the ultrasound images are renal stones or calculi, cyst and infections. A Graphical User Interface (GUI) is developed to display the count and size of calculi present in kidney. The GUI also specifies the presence of cyst. Ultrasound renal images are preprocessed and the speckle noises are removed using median filter. MATLAB software is used to perform the image processing. Segmentation is performed using Thresholding and Seeded Region growing algorithms. The algorithms are compared based on the statistical features which are extracted from the segmented images and the Seeded Region Growing algorithm is found to be the best since the Mean Square Error (MSE) and Standard Deviation (SD) are comparatively less. The appropriate treatment procedure obtained from an expert urologist is suggested in the GUI depending upon the size of the calculi.
Keywords: Mean Square Error, Seeded Region Growing Algorithm, Standard Deviation, Thresholding Algorithm, Ultrasound Scan
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