Advances in Breast Cancer Research, 2013, 2, 72-77 doi:10.4236/abcr.2013.23013 Published Online July 2013 (http://www.scirp.org/journal/abcr) Mammogram Images Thresholding for Breast Cancer Detection Using Different Thresholding Methods Moumena Al-Bayati, Ali El-Zaart Department of Mathematics and Computer Science, Beirut Arab University, Beirut, Lebanon Email: Moumena.alhadithi@yahoo.com, dr_elzaart@yahoo.com Received April 2, 2013; revised May 5, 2013; accepted May 13, 2013 Copyright © 2013 Moumena Al-Bayati, Ali El-Zaart. This is an open access article distributed under the Creative Commons Attribu- tion License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT The purpose of this study is to apply different thresholding in mammogram images, and then we will determine which technique is the best in thresholding (extraction) malignant and benign tumors from the rest breast tissues. The used technique is Otsu method, because it is one of the most effective methods for most real world views with regard to uni- formity and shape measures. Also, we present all the thresholding methods that used the concept of between class vari- ance. We found from the experimental results that all the used thresholding techniques work well in detection normal breast tissues. But in abnormal tissues (breast tumors), we found that only neighborhood valley emphasis method gave best detection of malignant tumors. Also, the results demonstrate that variance and intensity contrast technique is the best in extraction the micro calcifications which represent the first signs of breast cancer. Keywords: Breast Cancer; Mammogram; Segmentation; Threshold; Otsu Method 1. Introduction Normal cells of the human body grow and divide to ge- nerate new cells in order to meet the body needs. If these normal cells grow old or destroyed, they die. So the new cells take their place. Cancer occurs when these new cells are generated and the body does not need them; in addition, the old and destroyed cells do not die as they should, so these additional cells are constructed as a mass tissue known as a lump, growth or tumor [1]. One of the main lethal cancers is breast cancer [1,2]. It occurs in both males and females, but the breast cancer in men is scarce [1,3]. Around 25% of all cancers in woman are breast cancer, and approximately 20% of cancers caus- ing death are breast cancers. It primarily sites in ducts (tubas that transfers milk to the nipple) and in the lobules (glands which make milk) in the breast by taking the form as micro-calcifications or masses [1]. Commonly, breast cancer cannot be avoidable, but at least early detection gives a big opportunity of the treatment of this disease [2]. One of the best tools for scanning the structure of the breast is mammogram [1,2]. Mammogram images are classified depended on the categories as malignant, be- nign and normal. Where benign and malignant are ab- normal; benign tumors can be treated (not cancerous). In contrast, to malignant tumors (cancerous), this can dam- age neighboring tissues and spread to the rest parts of body [1,2]. Mammogram images are represented as very accurate and complex images to be interpreted [1,3]. More- over, many cases (about 25%) of cancers are not detected in the screen. Therefore, there is a necessary need to im- prove computer-aided diagnosis (CAD) systems to help a radiologist in his diagnosis for the cancer [3,4]. For these reasons, our aim in this paper is to put the scope on ap- plying different thresholding techniques on mammogram images as a try to determine which one presents best re- sults. This paper is organized as follows: Section 2 is a brief introduction of segmentation, and mention types of seg- mentation used in mammograms. It also defined the thresholding, and related works for thresholding used in mammogram images. Section 3 illustrated the formula- tion of the structure of an image. Section 4 describes Otsu method, and the techniques related to it. Section 5 is about the thresholding evaluation methods. Section 6 is a discussion for experimental results. Conclusions appear in Section 7. 2. Thresholding Segmentation in image processing plays a central role in detection the region of interest from background. Its in- Copyright © 2013 SciRes. ABCR