Segmentation of Ultrasound Abdominal Images to extract Region of Interest
Ranjitha M, Department of Computer Science, Kristu Jayanti College (Autonomous), Bengaluru, India.
Manuscript received on 12 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 5480-5483 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2469078219/19©BEIESP | DOI: 10.35940/ijrte.B2469.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Ultrasound imaging is one of the safest techniques for disease diagnosis which can be used in any part of the body. One of the major reason for using ultrasound images is the cost when compared with MRI, PET etc. Further, it is free from any radiation exposure and is an efficient technique for initial diagnosis. This paper concentrates on segmentation of kidney from abdominal ultrasound images. There are many common ailments affecting kidney and hence conducting study on this segmented image becomes easy with an efficient segmentation technique. Various algorithms to pull out kidney regions from abdominal ultrasound images which are discussed by many researchers are also investigated in this paper. One of the major drawback of ultrasound image is that due to the complicated internal organs of the abdominal region, extraction of only kidney region is very challenging. This paper proposes a new technique where the collected abdominal ultrasound image is cleaned, to remove unwanted noise produced due to various interferences. After applying the filtering technique, kidney region is segmented. This extracted kidney image is subjected to Region indicator contour segmentation method to extract the renal calculi which is the region of interest in this study. The method is experimented with a reasonable number of dataset and applied the statistical performance test to check for the accuracy.
Keywords: Ultrasound Images, Kidney, Renal Calculi, Segmentation, Noise Removal.
Scope of the Article: Image Processing and Pattern Recognition