Appendix Image Analysis

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Appendicitis leftovers the commonest cause of lower abdominal pain. It retains a common appearance at all ages. The appendix is an attachment or adjunct like structure. It is a wormlike abdominal diverticulum extending from the blind end of the cecum; it varies in length and ends in a blind extremity. Early and accurate analysis of appendicitis can decrease the illness and hospital cost by reducing the delay in diagnosis of appendicitis and its associated complications. Accurate diagnosis of appendicitis is a difficult problem in practice especially if the patient is too young or pregnant women in that radiological test have high risk. Thus, ultrasonography image analysis is a good way to reduce the difficulty. This work presents the
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Milton Wider, Yin M. Myint et al [8], discusses the comparative results of three thresholding segmentation methods. From this comparison it can be clearly seen that the proposed method is the most appropriate method for appendix image segmentation. When analyzed the extracted appendix image, it can be concluded that the normal probe view is the best transducer position. The Appendix ultrasound images with five different probe positions are taken using Aplio MX, Toshiba ultrasound machine available in the lab.
J. Lam, C. Pahl, et al [9], proposed a series of image processing method including image enhancement, image segmentation and edge detection before measuring the appendiceal. Selection of image enhancement method is made based on MSE and PSNR values while selection of image segmentation method is made based on the segmented image and execution time. Ten trials of measurement by sonographers using ultrasound and measurement after image processing were gathered. Statistical analyses of both measurements were computed. Mean and standard deviation for the sonographers measurements and measurements after image processing are 4.937±0.14mm and 4.613710±0.08mm respectively. Sonographers measurement showed higher variability compared to measurement after image processing thus measuring the appendiceal diameter after image processing can be helpful for a better diagnosis. The appendix ultrasound images were taken using Aplio MX, Toshiba ultrasound machine available
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Balu, T. Devi, et al [11], describes the image mining system that automates the diagnosis of acute appendicitis with significant time reduction. The experimentation methods, results of the testing using real data are detailed in this paper. The data set of 44 patients’ sonographic images collected from a reputed hospital in India has been used as input. The conclusion is that region based segmentation algorithm followed by Euclidean distance method yields accurate diagnosis of appendicitis. The developed sonographic image mining system to detected acute appendicitis yielded a sensitivity of 86% and specificity of
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