Boundary Detection to Segment the Pectoral Muscle from Digital Mammograms Images
Sameer Saxena1, Yudhveer Singh2, Basant Agarwal3

1Sameer Saxena*, AIIT, Amity University Rajasthan, Jaipur, India.
2Dr. Yudhvir Singh, AIIT, Amity University Rajasthan, Jaipur, India.
3Dr. Basant Agarwal, Department of CSE, IIIT Kota, Jaipur, India.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1593-1603 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5423029320/2020©BEIESP | DOI: 10.35940/ijeat.C5423.029320
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Abstract: Breast cancer is class of cancer that sets off when the breast cells grow out of proportion and control. The radiologist recognizes the sign of breast cancer by performing a kind of X-ray called screening mammography. During analysis of mammography the biggest problem arise because of the presence of pectoral muscle. The mass of tissue on which the breast is rest called the Pectoral muscle. The primary problem is that pectoral muscle area density is almost similar to the tumour cell and this condition generates confusion to recognize the tumour cell. For analysis the Medio-Lateral oblique (MLO) views of mammograms is being taken so that the complete breast image can be viewed. Some part of the pectoral muscle also gets visible along with the breast in the MLO view which must be segmented from the mammogram. Pectoral muscle involvement can lead to false positive or false negatives. The workforce shortages of Radiologist with respect to growing demands and to declare the result in a very short time have also increased the pressure. Consequently, a radiologist is sometimes unable to detect an anomaly. This is the time where CAD system can help radiologists to detect breast cancer at an earlier stage. Numerous strategies for the selection of the pectoral muscle have been suggested and developed so far. This article reviews the different segmentation techniques for pectoral muscle removal in mammograms through digital images.
Keywords: Pectoral muscle, Mammography, Computer-aided diagnosis, Segmentation.