Automatic assessment of mammographic positioning on the mediolateral oblique view

• Published in: 2004 International Conference on Image Processing, 2004. ICIP '04 Vol. 1; pp. 151 - 154 Vol. 1
• Main Authors: Sze Man Kwok, Chandrasekhar, R.A., Attikiouzel, Y.
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Artificial intelligence; Breast cancer; Breast tissue; Cancer detection; Computer science; control theory; systems; Exact sciences and technology; Image coding; Lesions; Muscles; Pattern recognition. Digital image processing. Computational geometry; Sensitivity and specificity; X-ray detection; X-ray detectors; X-ray imaging; Human; Positioning; Image processing; X ray radiography; Mammography; Malignant tumor; Tissue; Cancerology; High sensitivity; Quality control; Database; Mammary gland; Localization
• ISBN: 0780385543; 9780780385542
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2004.1418712

Mammograms are X-ray images of the compressed breast and are widely used for early detection of breast cancer. A mammogram must be of sufficient quality for the radiologist to detect lesions or other abnormalities with high sensitivity and specificity. Some algorithms are presented for the automatic assessment of the quality of positioning on mediolateral oblique (MLO) view mammograms. Anatomic features, including the breast border, nipple location and pectoral margin, were first extracted from each image. Then several quality criteria, including breast tissue exclusion, nipple in profile, inclusion of inframammary fold, and positioning of the pectoral muscle, were used to assess the adequacy of breast positioning. The assessment method was tested on 322 digitized mammograms in the MIAS database.