International Journal of Engineering Research in Electronics and Communication Engineering

Detection of Breast Cancer Using Mammogram Classification by Using 2 DWT & GLCM

Author : Varad Mayee ¹ P.B.Bhalerao ²

Date of Publication :14th February 2018

Abstract: Breast cancer is found to be the most common form of cancer found in women which is the leading cause of cancer death worldwide. Detection of abnormality at the earliest increases the chances of successful treatment and can reduce the mortality rate. MRI is a widely used medical imaging technique. Noise in MRI negatively affects image processing and analysis works. The main objective of the pre-processing stage is to improve the quality of an image by removing the irrelevant noises and unwanted portions in the image so as to convert the image into some other representation that is more meaningful, thus making it easier to interpret the details in an image. In this proposed work various filtering algorithms are discussed and compared and an automated scheme for Magnetic Resonance Imaging (MRI) breast segmentation is proposed. It is found that there are several types of abnormalities in breast. Among those, signs of breast cancer are normally associated with asymmetry between images of left and right breasts. Another type of abnormalities related to breast tumors is the presence of micro-calcifications in the breast, the presence of masses in the breast and Architectural Distortion (AD). Architectural Distortion refers to, disruption of the normal arrangement of the tissue strands of the breast resulting in a radiating or haphazard pattern without an associated visible centre. Micro-calcifications (MC) are tiny deposits that range from 50 to several hundred microns in diameter, which usually appear in clusters. Masses are signs of breast cancer. Masses with speculated margins have a high likelihood of malignancy. Architectural distortion (AD) is the third most common mammographic finding of breast cancer. Literature informs that about 81% of the speculated mass and 48- 60% of an AD is malignant and it is estimated that 12-45% of cancers not found in mammographic screening are the AD. The detection sensitivity of the current computer systems for v speculated mass and AD is not as effective as micro-calcification detection algorithms and thus there is a pressing need for improvements in their detection

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