https://scholars.lib.ntu.edu.tw/handle/123456789/477743
標題: | Quantitative analysis of breast echotexture patterns in automated breast ultrasound images | 作者: | Chang R.-F. Hou Y.-L. Lo C.-M. CHIUN-SHENG HUANG Chen J.-H. Kim W.H. Chang J.M. Bae M.S. Moon W.K. |
公開日期: | 2015 | 出版社: | AAPM - American Association of Physicists in Medicine | 卷: | 42 | 期: | 8 | 起(迄)頁: | 4566-4578 | 來源出版物: | Medical Physics | 摘要: | Purpose: Breast tissue composition is considered to be associated with breast cancer risk. This study aimed to develop a computer-aided classification (CAC) system to automatically classify echotexture patterns as heterogeneous or homogeneous using automated breast ultrasound (ABUS) images. Methods: A CAC system was proposed that can recognize breast echotexture patterns in ABUS images. For each case, the echotexture pattern was assessed by two expert radiologists and classified as heterogeneous or homogeneous. After neutrosophic image transformation and fuzzy c-mean clusterings, the lower and upper boundaries of the fibroglandular tissues were defined. Then, the number of hypoechoic regions and histogram features were extracted from the fibroglandular tissues, and the support vector machine model with the leave-one-out cross-validation method was utilized as the classifier. The authors' database included a total of 208 ABUS images of the breasts of 104 females. Results: The accuracies of the proposed system for the classification of heterogeneous and homogeneous echotexture patterns were 93.48% (43/46) and 92.59% (150/162), respectively, with an overall Az (area under the receiver operating characteristic curve) of 0.9786. The agreement between the radiologists and the proposed system was almost perfect, with a kappa value of 0.814. Conclusions: The use of ABUS and the proposed method can provide quantitative information on the echotexture patterns of the breast and can be used to evaluate whether breast echotexture patterns are associated with breast cancer risk in the future. ? 2015 American Association of Physicists in Medicine. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937047005&doi=10.1118%2f1.4923754&partnerID=40&md5=cbb68dd3c56e2ca4d3c2e10b3e102e70 https://scholars.lib.ntu.edu.tw/handle/123456789/477743 |
ISSN: | 0094-2405 | DOI: | 10.1118/1.4923754 | SDG/關鍵字: | Automation; Diseases; Histology; Statistical methods; Support vector machines; Tissue; Ultrasonics; Breast Cancer; Breast ultrasound; Computer Aided Classification; echotexture; Mammographic density; Medical imaging; adult; aged; area under the curve; Article; breast cancer; breast density; breast echotexture pattern; cancer risk; computer aided classification; computer aided design; computer assisted diagnosis; controlled study; digital mammography; echomammography; echomammography device; entropy; female; fuzzy system; histogram; human; human tissue; image processing; major clinical study; pattern recognition; predictive value; probability; quantitative analysis; radiological parameters; receiver operating characteristic; retrospective study; sensitivity and specificity; statistical significance; support vector machine; breast tumor; classification; cluster analysis; computer assisted diagnosis; echography; echomammography; factual database; middle aged; procedures; support vector machine; young adult; Adult; Aged; Area Under Curve; Breast Neoplasms; Cluster Analysis; Databases, Factual; Female; Humans; Image Interpretation, Computer-Assisted; Middle Aged; Retrospective Studies; ROC Curve; Sensitivity and Specificity; Support Vector Machine; Ultrasonography, Mammary; Young Adult |
顯示於: | 醫學系 |
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