Pectoral Muscle removal and Detection of masses in Digital Mammogram using CCL

Abstract

A mammogram is a radiograph of the breast tissue. It is an effective non-invasive means of examining the breast, commonly searching for breast cancer. Cancer is not preventable, but early detection leads to a much higher chance of recovery and lowers the mortality rate. Due to the high volume of images to be analyzed by radiologists, and since senior radiologists are rare, the accuracy rate tends to decrease. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or poor diagnosis. This paper proposes a computer aided diagnosis system for detecting masses in mammograms using connected component labeling(CCL). This paper also addresses the problem of eliminating and pectoral muscles from the mammogram before the detection process so that further processing is confined to the breast region alone.

Removal of Pectoral Muscle in Mammograms using Statistical Parameters

“…Extracting the pectoral muscle [15,16,17] is particularly important in automated mammogram image assessment. Segmentation of the pectoral muscle is a non-trivial, complex and demanding task.…”

Deep Learning Based Pectoral Muscle Segmentation on MIAS Mammograms

“…However, the results can be di cult to interpret for dense breasts, which leads to a high risk of misdiagnosis [2]. For this reason, there has been ongoing research on computer-aided diagnosis (CAD) systems, in order to reduce the number of misdiagnoses and to improve the accuracy of diagnosis by radiologists using mammography [3][4][5]. CAD systems use computer algorithms to enable objective and accurate detection of lesions that are di cult to distinguish with the naked eye.…”

Performance Enhancement of Mammogram Preprocessing for Breast Cancer Detection using Improved Successive Mean Quantization Technique

“…3) Combining spatial and frequency domains. [9][10][11][12][13][14] and image histograms [15][16][17].…”