Classification of malignant and benign tumors using boundary characteristics in breast ultrasonograms.
We evaluated various spiculate and jagged margin shape features. These are known to be malignant characteristics in breast sonograms. A total of 79 breast ultrasonograms (60 benign, 19 malignant) containing solid breast nodules were evaluated. To determine the boundary of lesions, Markov random field segmentation was used. Our goal was to classify benign and malignant lesions on the breast sonogram. Our algorithm consisted of two steps: segmentation and classification. In the first step, a breast sonogram was segmented using low resolution and Gaussian-Markov random field. The fuzzy clustering method algorithm was then applied to the preprocessed image to initialize the segmentation. Next, to discriminate benign and malignant tumors three types of lesion characteristics were investigated: jag count, compactness, and acutance. Jag count was calculated based on the derivative of curvature, acutance was defined as gray-level variations across the lesion boundary, and compactness was defined as the ratio of boundary complexity to the enclosed area. Sensitivity of the three boundary features (jag count, compactness, and acutance) was 95.1, 94.1, and 81.1%, respectively, and their specificities were 97.2, 92.0, and 78%, respectively. The jag count performed best among the three boundary features. Our results indicate that computerized analysis of boundary characteristics can be an effective method for classifying solid breast nodules in ultrasonograms as malignant or benign. We found that curvature analysis was the best shape features. The curvature method classifies better than the compactness or acutance methods.
Kim
Kwang Gi
KG
Interdisciplinary Program in Medical and Biological Engineering, Department of Radiology and Biomedical Engineering, Seoul National University, Korea. kimjh@radcom.snu.ac.kr
Kim
Jong Hyo
JH
Min
Byoung Goo
BG
D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary