K2. Automatic pectoral muscle boundary detection in mammograms using eigenvectors segmentation

Mammograms are X-ray images, which are used in breast cancer detection. Automatic pectoral muscle removal on Medio-Lateral Oblique-view (MLO) of mammograms is an essential step for many mammography processing algorithms. Presence of pectoral muscle gives false positive results in automated breast ca...

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Bibliographic Details
Published in2012 29th National Radio Science Conference (NRSC) pp. 633 - 640
Main Authors Abdellatif, H., Taha, T. E., Zahran, O. F., Al-Nauimy, W., El-Samie, F. E. A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2012
Subjects
Breast cancer
Educational institutions
Image edge detection
Image segmentation
Mammograms
Muscles
Normalized graph cuts
Pectoral muscle
Vectors
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Summary:Mammograms are X-ray images, which are used in breast cancer detection. Automatic pectoral muscle removal on Medio-Lateral Oblique-view (MLO) of mammograms is an essential step for many mammography processing algorithms. Presence of pectoral muscle gives false positive results in automated breast cancer detection. The sizes, the shapes and the intensity contrasts of pectoral muscles change greatly from one MLO view to another. So, the suppression or exclusion of the pectoral muscle from the mammograms is demanded for computer-aided analysis, and this task requires the identification of the pectoral muscle. The main objective of this study is to propose an automated method to efficiently identify the pectoral muscle in MLO mammograms. This work uses a normalized graph cuts segmentation technique for identifying the pectoral muscle edge.
ISBN:1467318841
9781467318846
DOI:10.1109/NRSC.2012.6208576