Particle Swarm Optimization Based Contrast Limited Enhancement for Mammogram Images Shelda Mohan and T.R. Mahesh Department of Computer science and Engineering, T John institute of Technology, Bangalore, India Abstract—In the present medical scenario detection of breast cancer in its early stage is a very immense challenge. Many histogram based enhancement are present today. In this paper a Particle Swarm Optimization (PSO) for tuning the enhancement parameter of Contrast Limited Adaptive Histogram Equalization (CLAHE) based on Local Contrast Modification (LCM) is presented. The PSO method of parameter tuning adopted for LCM-CLAHE enhancement for mammogram images achieves very good quality of images compared to other exiting methods. The quality of enhanced image is tested using an efficient objective criteria based on entropy and edge information of the image. Results are compared with other enhancement techniques such as histogram equalization, unsharpmasking. The performance of this method is tested using Peak Signal to Noise Ratio. The quality of image shows that image obtained after this method can be useful for efficient detection of breast cancer in further process like segmentation, classification etc. Keywords: Particle Swarm Optimization, Parameter Tuning, Local Contrast Modification (LCM), CLAHE, PSNR, UnsharpMasking, HE. I. INTRODUCTION Breast cancer ranks second to lung cancer and is the most common form of malignancy in women. Breast cancer impacts over 240,000 new patients a year in the United States alone. One in eight women in United States will develop breast cancer during her lifetime [1]. 70% of breast cancer cases occur in women who have no identifiable risk factors [2]. It is a pernicious disease that is causing large numbers of deaths not only in developed countries like the United States of America, United Kingdom, Australia and Canada but also in the underdeveloped and developing countries including India. It occurs most commonly amongst women in the age group of 40 to 50 years of age. As the incidence of this disease is increasing all over the world, it is therefore an extremely important public health objective to be able to detect the disease at the earliest possible stage. Even with the advancement in medical technology it is complex to detect cancerous cells in its premature stage. In breast cancer detection the critical part is a method to distinguish between normal tissues and cancerous tissues. Differentiating of this by human eye is very hard. Mammography is the primary 978-1-4673-4601-6/13/$25.00 © 2013 IEEE method in the detection of breast cancer, and it is an X-Ray imaging technique. Mammography is very effective method of finding breast diseases. Even with this effective method over 10 percent of the cancerous lesions are left undetected and also has the drawbacks like low contrast images [3]. In order to deal with the low quality or low contrast X-Ray images, Image Enhancement is used. The goal of image enhancement algorithm is to get finer details of an image and highlight the useful information that is not clearly visible in the original image. Enormous research is being done in the image enhancement in medical field and various techniques has been developed which improved the image quality to a certain extend [4]. II. RELATED WORKS Many histogram based enhancement are present today. The Histogram Equalization (HE) which is one of the popular methods for contrast enhancement modifies the gray level histogram of an image to a uniform distribution [4]. But in many cases it produces over enhancement in output image and loss of local information which leads to insufficient medical details during diagnosis. To overcome these drawbacks, many variants of HE have been proposed [5-8]. In medical imaging (such as mammogram enhancement) local contrast are more important than global contrast. In such type of applications Global Histogram Equalization (GHE) is insufficient because it cannot deal with local features of original image due to its global nature. Adaptive Histogram Equalization (AHE) method will perform throughout all pixels in the entire image and maps gray level using local histograms, but it takes more time [5]. Pizer has proposed AHE in which the input image is divided into blocks and then the mapping functions are computed for those blocks using CLAHE [7]. M. Sundaram has proposed a method for image enhancement based on Histogram Modification and Local Contrast Enhancement which uses an enhancement parameter for adjusting the contrast of the image. The enhancement parameter is selecting manually in the above work [9]. The Histogram Modified Contrast Limited Adaptive Histogram Equalization provides an option for adjusting the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation [10]. Apurba-Gorai