A decision support system has been developed to assist the radiologist during mammogram classification. In this paper, mass identification and segmentation methods are discussed in brief. Fuzzy region-growing techniques are applied to effectively segment the tumour candidate from surrounding breast tissue. Boundary extraction is implemented using a unit vector rotating about the mass core. The focus of this work is on the feature extraction and classification processes. Important information relating to the malignancy of a mass may be derived from its morphological properties. Mass shape and boundary roughness are primary features used in this research to discriminate between the two types of lesions. A subset from thirteen shape descriptors is input to a binary decision tree classifier that provides a final diagnosis of tumour malignancy. Features that combine to produce the most accurate result in distinguishing between malignant and benign lesions include: spiculation index, zero crossings, boundary roughness index and area-to-perimeter ratio. Using this method, a classification result of high sensitivity and specificity is achieved, with false-positive and falsenegative rates of 9.3% and 0% respectively.

Date Published: 12 May 2004
PDF: 11 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.533938

Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)