Name
Abstract | ||
|---|---|---|
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 false-negative rates of 9.3% and 0% respectively. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1117/12.533938 | Proceedings of SPIE |
Keywords | Field | DocType |
mammogram,classification,spiculation,region-growing,false-positive,false-negative,morphology,segmentation,feature extraction | Breast cancer classification,Mammography,Data mining,Pattern recognition,Segmentation,Fuzzy logic,Feature extraction,Artificial intelligence,Region growing,Classifier (linguistics),Mathematics,Shape analysis (digital geometry) | Conference |
Volume | ISSN | Citations |
5370 | 0277-786X | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| C. A. Todd | 1 | 18 | 6.10 |
| Golshah Naghdy | 2 | 29 | 9.36 |