Classification of benign and malignant breast tumors by 2-d analysis based on contour description and scatterer characterization.
Ultrasound B-mode scanning based on the echo intensity has become an important clinical tool for routine breast screening. The efficacy of the Nakagami parametric image based on the distribution of the backscattered signals for quantifying properties of breast tissue was recently evaluated. The B-mode and Nakagami images reflect different physical characteristic of breast tumors: the former describes the contour features, and the latter reflects the scatterer arrangement inside a tumor. The functional complementation of these two images encouraged us to propose a novel method of 2-D analysis based on describing the contour using the B-mode image and the scatterer properties using the Nakagami image, which may provide useful clues for classifying benign and malignant tumors. To validate this concept, raw data were acquired from 60 clinical cases, and five contour feature parameters (tumor circularity, standard deviation of the normalized radial length, area ratio, roughness index, and standard deviation of the shortest distance) and the Nakagami parameters of benign and malignant tumors were calculated. The receiver operating characteristic curve and fuzzy c-means clustering were used to evaluate the performances of combining the parameters in classifying tumors. The clinical results demonstrated the presence of a tradeoff between the sensitivity and specificity when either using a single parameter or combining two contour parameters to discriminate between benign and malignant cases. However, combining the contour parameters and the Nakagami parameter produces sensitivity and specificity that simultaneously exceed 80%, which means that the functional complementation from the B-scan and the Nakagami image indeed enhances the performance in diagnosing breast tumors.
Tsui
Po-Hsiang
PH
Division of Mechanics, Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan. tsuiph@gate.sinica.edu.tw
Liao
Yin-Yin
YY
Chang
Chien-Cheng
CC
Kuo
Wen-Hung
WH
Chang
King-Jen
KJ
Yeh
Chih-Kuang
CK
D000465:Algorithms; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging / Q000473:pathology; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000379:methods*