https://scholars.lib.ntu.edu.tw/handle/123456789/477808
標題: | Breast Tumor Classification Using Fuzzy Clustering for Breast Elastography | 作者: | Moon W.K. Chang S.-C. CHIUN-SHENG HUANG Chang R.-F. |
公開日期: | 2011 | 卷: | 37 | 期: | 5 | 起(迄)頁: | 700-708 | 來源出版物: | Ultrasound in Medicine and Biology | 摘要: | Elastography is a new ultrasound imaging technique to provide the information about relative tissue stiffness. The elasticity information provided by this dynamic imaging method has proven to be helpful in distinguishing benign and malignant breast tumors. In previous studies for computer-aided diagnosis (CAD), the tumor contour was manually segmented and each pixel in the elastogram was classified into hard or soft tissue using the simple thresholding technique. In this paper, the tumor contour was automatically segmented by the level set method to provide more objective and reliable tumor contour for CAD. Moreover, the elasticity of each pixel inside each tumor was classified by the fuzzy c-means clustering technique to obtain a more precise diagnostic result. The test elastography database included 66 benign and 31 malignant biopsy-proven tumors. In the experiments, the accuracy, sensitivity, specificity and the area index Az under the receiver operating characteristic curve for the classification of solid breast masses were 83.5% (81/97), 83.9% (26/31), 83.3% (55/66) and 0.902 for the fuzzy c-means clustering method, respectively, and 59.8% (58/97), 96.8% (30/31), 42.4% (28/66) and 0.818 for the conventional thresholding method, respectively. The differences of accuracy, specificity and Az value were statistically significant (p < 0.05). We conclude that the proposed method has the potential to provide a CAD tool to help physicians to more reliably and objectively diagnose breast tumors using elastography.(E-mail: rfchang@csie.ntu.edu.tw). ? 2011 World Federation for Ultrasound in Medicine & Biology. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-79954628324&doi=10.1016%2fj.ultrasmedbio.2011.02.003&partnerID=40&md5=6f594dfac1e732fc3010cd4aee379428 https://scholars.lib.ntu.edu.tw/handle/123456789/477808 |
ISSN: | 0301-5629 | DOI: | 10.1016/j.ultrasmedbio.2011.02.003 | SDG/關鍵字: | Area index; Breast tumor; CAD; CAD tool; Dynamic imaging; Elastograms; Elastography; Fuzzy c-means clustering; Fuzzy c-means clustering method; Level Set method; Receiver operating characteristic curves; Soft tissue; Solid breast mass; Thresholding methods; Thresholding techniques; Tissue stiffness; Ultrasound imaging; Cluster analysis; Computer aided design; Computer aided diagnosis; Elasticity; Fuzzy clustering; Fuzzy systems; Numerical methods; Pixels; Stiffness; Ultrasonic imaging; Tumors; adult; aged; article; breast cancer; breast tumor; computer assisted diagnosis; controlled study; data base; diagnostic accuracy; elasticity; elastography; female; fuzzy system; histopathology; human; human tissue; major clinical study; prediction; priority journal; reliability; rigidity; sensitivity and specificity; solid tumor; tumor biopsy; tumor classification; Adult; Aged; Algorithms; Breast Neoplasms; Elasticity Imaging Techniques; Female; Humans; Middle Aged; Sensitivity and Specificity; Ultrasonography, Mammary |
顯示於: | 醫學系 |
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