Background: This paper attempts to pinpoint different techniques for Pectoral Muscle
(PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms.
The segmentation of PM and detection of MC and its classification are mostly based on image
processing and data mining techniques.
Discussion: The review centered on major techniques in image processing and data mining that is
employed for PM segmentation, MC detection and classification in digital mammograms. Breast
cancer is one of the significant causes of death among women aged above 40. Mammography is
considered the most successful means for prompt and timely detection of breast cancers. One notable
visual indication of the malignant growth is the appearance of Masses, Architectural Distortions,
and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind
viewers, and it is hard for radiologists to supply both precise and steady assessment for a large
number of mammograms created in extensive screening. Computer Aided Detection has been employed
to help radiologists in detecting MC and MCCs. The automatic recognition of malignant
MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of
automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation
techniques are also summarized.
Conclusion: The techniques used for segmentation of PM, MC detection and classification in a
digitized mammogram are reviewed.