@article{oai:dmu.repo.nii.ac.jp:00000750,
 author = {Takada, Etsuo and Chang, Ruey-Feng and Shen, Wei-Chih and Ho, Yu-Chun and Woo, Kyung Moon and Nakajima, Michiko and Kobayashi, Masayuki},
 issue = {1},
 journal = {Dokkyo journal of medical sciences},
 month = {Mar},
 note = {Ultrasonography has been an important imaging technique for detecting breast tumors. As opposed tothe conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imagingthe elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicatesthe soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy.In this paper, the authors proposed a computer-aided diagnosis( CAD) system on elastography tomeasure whether this system is effective and accurate to classify the tumor into benign and malignant. Accordingto the features of elasticity, the color elastography was transferred to hue, saturation, and value(HSV) color space and extracted meaningful features from hue images. Then the neural network was utilizedin multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including113 benign and 67 malignant cases used to examine the classification. The results of the proposedsystem showed an accuracy of 83.89 %, a sensitivity of 82.09 % and a specificity of 84.96 %. Compared withthe physician's diagnosis, an accuracy of 78.33 %, a sensitivity of 53.73 % and a specificity of 92.92 %, theproposed CAD system had better performance. Moreover, the agreement of the proposed CAD system andthe physician's diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreementof observers., 原著, Original},
 pages = {17--25},
 title = {Computer-aided Diagnosis of Breast Elastography},
 volume = {36},
 year = {2009}
}