https://scholars.lib.ntu.edu.tw/handle/123456789/390392
標題: | Computer-aided assessment of tumor grade for breast cancer in ultrasound images | 作者: | Chen, D.-R. Chien, C.-L. YAN-FU KUO |
公開日期: | 2015 | 卷: | 2015 | 來源出版物: | Computational and Mathematical Methods in Medicine | 摘要: | This study involved developing a computer-aided diagnosis (CAD) system for discriminating the grades of breast cancer tumors in ultrasound (US) images. Histological tumor grades of breast cancer lesions are standard prognostic indicators. Tumor grade information enables physicians to determine appropriate treatments for their patients. US imaging is a noninvasive approach to breast cancer examination. In this study, 148 3-dimensional US images of malignant breast tumors were obtained. Textural, morphological, ellipsoid fitting, and posterior acoustic features were quantified to characterize the tumor masses. A support vector machine was developed to classify breast tumor grades as either low or high. The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an AZ of 0.7940. ? 2015 Dar-Ren Chen et al. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84924874094&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/390392 |
DOI: | 10.1155/2015/914091 | SDG/關鍵字: | Diseases; Medical imaging; Support vector machines; Tumors; Ultrasonics; 3-dimensional; Acoustic features; Breast cancer tumors; Computer Aided Diagnosis(CAD); Computer-aided assessment; Ellipsoid-fitting; Prognostic indicators; Ultrasound images; Computer aided diagnosis; Article; breast biopsy; breast cancer; cancer grading; computer aided design; diagnostic accuracy; diagnostic test accuracy study; echography; genetic algorithm; human; human tissue; major clinical study; predictive value; sensitivity and specificity; support vector machine; three dimensional imaging; tumor biopsy; ultrasound scanner; ultrasound transducer; acoustics; adult; aged; automated pattern recognition; Breast Neoplasms; cancer grading; computer assisted diagnosis; female; image processing; middle aged; procedures; prognosis; receiver operating characteristic; very elderly; Acoustics; Adult; Aged; Aged, 80 and over; Breast Neoplasms; Diagnosis, Computer-Assisted; Female; Humans; Image Processing, Computer-Assisted; Middle Aged; Neoplasm Grading; Pattern Recognition, Automated; Predictive Value of Tests; Prognosis; ROC Curve; Support Vector Machine |
顯示於: | 生物機電工程學系 |
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