R. B. Dubey and Anju Dahiya/ Elixir Digital Processing 88 (2015) 36137-36155 36137 Introduction The ultrasound imaging is the most popular and cost effective imaging modality for treatment of kidney stone disease. Image segmentation is the low level image processing technique which aims to partition an image into regions such that each region groups pixels sharing similar attributes. Segmentation of nontrivial images is one of the most difficult tasks in image processing. Segmentation accuracy determines the eventual success or failure of computerized analysis procedures. For this reason, considerable care should be taken to improve the probability of accurate segmentation. Here we segment the image and detect the stones present in kidney. Individual errors may occur during the interpretation of ultrasound image by an untrained sonographer, while taking dimensions. Thus, for the purpose of avoiding the dependability to the sonographers’ expertise, some image processing operations are applied for segmentation and detection of stone. We use the evolutionary techniques for segmentation of the kidney stone ultrasound images based on electromagnetism optimization algorithm and harmony search algorithm for determining multilevel threshold for image. Segmenting the calculi from the kidney images is very useful for the medical diagnosis in analyzing the patient’s data. Considering the importance of the kidney stone segmentation, many research works are developed with different techniques to accomplish the kidney stone segmentation process. This work focuses on the detection of kidney stone using segmentation approaches which uses the ultrasound kidney image as input. The ultrasound images are very challenging and prone to speckle noise so images are pre-processed firstly using the image restoration methods and then segmented by the proposed segmentation approaches. Afterwards, the stone is extracted and the area of the detected stone is compared to calculate the error and accuracy of methods used [14, 16]. The remainder of this paper is organized as follows. Section 2 reviews the relevant previous literature and highlights the research motivation. Section 3 presents the materials and data set used. Section 4 summarizes the details of methodology. Section 5 provides results and discussions. Conclusions are drawn in Section 6. Related Work Kidney stone disease is very prevalent among Indians out of every 1000 Indian 3 people are suffering from kidney stone. The problem of stone can be due to various reasons such as food habits, salts present in drinking water and it could be genetic. Ultrasound imaging modality is one of popular method used by specialist to diagnose it. The reason behind the wide use of ultrasound images is because they are non-invasive, portable, radiation free, and affordable. Segmentation helps to detect and analyze the images which provide useful information regarding the progress of the disease [14]. The presence of speckle noise in ultrasound images is a problem which requires preprocessing of images before segmentation [13, 14, 16]. Normally, segmentation process is based on the image gray-level histogram, namely image histogram thresholding. The threshold based methods are parametric and non-parametric types. In parametric approaches, it is necessary to find some parameters like probability density function which models each class and these approaches are time consuming and computationally complex. Whereas the non-parametric involves use of several terms like between class variance, entropy, error rate etc. which is needed to be optimized to find an optimal threshold values. For bi-level thresholding the classical methods are Otsu method which chooses the optimal thresholds by maximizing the between class variance of gray levels and Kapur’s method finds the optimal threshold values by maximizing the entropy of histogram. Multilevel thresholding uses a number of thresholds in the histogram of the image to separate the pixels of the objects in the image. Although both Otsu and Kapur’s method can be expanded to multilevel thresholding but the problem lies in the selection of the optimal thresholds due to computational complexity which increases exponentially with Tele: 9971833006 E-mail addresses: rbdubeyster@gmail.com © 2015 Elixir All rights reserved A Comparison of Two Kidney Stone Segmentation Techniques R. B. Dubey and Anju Dahiya Hindu College of Engg, Sonepat, Haryana, India. ABSTRACT Kidney stone disease is very common among Indians and approximately 5-7 million patients suffer from stone disease. The most popular imaging modality for its treatment is ultrasound imaging. Segmentation accuracy determines the eventual success or failure of computerized analysis procedures. Traditional methods available for segmentation of kidney stones are not accurate. We use the evolutionary techniques for segmentation of the kidney stone ultrasound images for the first time based on electromagnetism optimization algorithm and harmony search algorithm for determining multilevel threshold of images. In most cases, the optimal thresholds are found by the minimizing or maximizing an objective function, which depends on the positions of the thresholds. We identify a class of objective functions for which the optimal thresholds can be found using algorithms with low time complexities. The Kapur’s entropy function and Otsu’s between class variance is maximized using these algorithms and an optimal threshold values at different levels are found out. The area of extracted stone is calculated and compared with the area given by the expert radiologist. The other metric used to compare the segmented image and the original image is the peak signal to-noise-ratio (PSNR) value. The relative error is also calculated to show the segmentation accuracy of each method. © 2015 Elixir All rights reserved. ARTICLE INFO Article history: Received: 22 September 2015; Received in revised form: 28 October 2015; Accepted: 03 November 2015; Keywords Kidney stone, Segmentation, Electromagnetism optimization algorithm, Harmony search algorithm. Elixir Digital Processing 88 (2015) 36137-36155 Digital Processing Available online at www.elixirpublishers.com (Elixir International Journal)