ISSN (Print) : 2320 – 3765 ISSN (Online): 2278 – 8875 International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 3, Special Issue 2, April 2014 Copyright to IJAREEIE www.ijareeie.com 644 Study of Mammogram Micro calcification to Aid tumour detection using Artificial Neural Network Based Classifier B.Vijayalakshmi 1 , R. Bhanumathi 2 , G.R. Suresh 3 PG Student, Dept. of CSE, Apollo Priyadarshanam Institute of Technology, Tamilnadu, India 1 Assistant professor, Dept. of CSE, Apollo Priyadarshanam Institute of Technology, Tamilnadu, India 2 Professor, Dept. of ECE, Easwari Engineering College, Tamilnadu, India 3 ABSTRACT: In mammograms, a cluster of micro calcifications shows an early sign of breast cancer. An accurate and efficient classification scheme is needed for detection of tumour in mammograms. A Computer-Aided-Detection (CAD) system for automatic detection of tumour of micro calcification clusters on mammograms is discussed in this paper. The approach is very helpful for radiologist to diagnose the tumour and performs faster than typical screening programs. The images are collected from the Mammographic Image Analysis Society (MIAS) databases for implementation. The feature is extracted by Local Binary Pattern and classified using ANN. The proposed scheme provides high accuracy in classification of micro calcifications. KEYWORDS: Artificial Neural Network, GLCM, Local Binary Pattern, Micro calcifications, Texture. I.INTRODUCTION For years cancer has been one of the biggest threats in human life, deaths caused by cancer are expected to increase in the future with an estimated 12 million people dying from cancer in 2030. Treatment of breast cancer at an early stage can significantly improve the survival rate of patients. Mammography is currently the most sensitive method for detecting early breast cancer. Retrospective studies have shown that radiologists can miss the detection of a significant proportion of abnormalities in addition to having high rates of false positives. The estimated sensitivity of radiologists in breast cancer screening is only about 75%. In order to improve the accuracy of interpretation, a variety of Computer- Assisted Detection (CAD) techniques have been proposed. In real sense, the Malignancy [1] or Benign, its type and from it detection of stage of cancer as invasive and non-invasive is a very fuzzy kind of decision making. Benign tumors are "well-differentiated," that the tumor cells differ only slightly in appearance and behavior from their tissue of origin. Malignant is used to describe a cancer that generally grows rapidly and is capable of spreading throughout the body. Detection of breast cancer is conducted by means of mammography and ultrasonography (USG) imaging. Microcalcifications (MCCs) clusters are one of the important radiographic indications related to breast cancer because they are present in 30%–50% of cancers found mammographic ally MCCs are tiny bits of calcium that may show up in clusters or in patterns (like circles) and are associated with extra cell activity in breast tissue. Scattered micro- calcifications are usually a sign of benign breast tissue. MCCs appear as small bright arbitrarily shaped regions on the large variety of breast texture background and characterize early breast cancer are detectable in mammograms shown in Fig1. For MCCs, the interpretations of their presence are very difficult because of its morphological features. The dense tissues especially in younger women may easily be misinterpreted as MCCs due to film emulsion error, digitization artefacts or anatomical structures such as fibrous strands, breast borders or hypertrophied lobules that almost similar to MCCs. Other factors that contribute to the difficulty of MCCs detection are due to their fuzzy nature, low contrast and low distinguish ability from their surroundings. However, microcalcification detection from mammograms may be troublesome. To overcome this problem, CAD is developed to improve the diagnostic accuracy and the consistency of the radiologists’ image interpretation. Issam El-