Application of computer-aided detection in ultrasound diagnosis of benign and malignant breast tumors(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第3期
- Page:
- 374-378
- Research Field:
- 其他（激光医学等）
- Publishing date:
Info
- Title:
- Application of computer-aided detection in ultrasound diagnosis of benign and malignant breast tumors
- Author(s):
- WU Xiuming1; WANG Xiali2; LYU Guorong2; WEI Mengwan3; DU Yongzhao2; 3; LIU Peizhong2; 3
- 1. Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, China; 2. Collaborative Innovation Center for Maternal and Infant Health Service Application Technology, Quanzhou Medical College, Quanzhou 362000, China; 3. Engineering Institute, Huaqiao University, Quanzhou 362000, China
- Keywords:
- Keywords: computer-aided detection; ultrasound; breast tumor; feature extraction; support vector machine classifier
- PACS:
- R319；R737.9
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.03.022
- Abstract:
- Abstract: Objective To explore the diagnostic value of computer-aided detection in breast tumors based on the detection of benign and malignant tumors and feature extraction. Methods The ultrasound images of 617 patients with breast tumors detected by ultrasound and confirmed by pathology were analyzed retrospectively. The regions of interest and lesion contours in the breast ultrasound images were obtained by manual extraction, and then 3 features, namely histogram of oriented gradient (HOG), local binary pattern (LBP) and gray level co-occurrence matrix (GLCM), were used to detect the true or false positive of benign and malignant breast tumors. Finally, receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of each feature for two types of lesions, and the diagnostic performance of feature set for classification. Results The diagnostic performance and area under ROC curve (AUC) of the detection with the combination of multiple features were superior to those obtained by every single feature (LBP, HOG or GLCM) (all P<0.05). The detection with the combination of multiple features had a sensibility similar to that of manual diagnosis, and a significantly increased specificity which was up to 98.57% (Z value=2.25, P<0.05), and a AUC of 0.985 which was obviously higher than 0.910 of manual diagnosis (Z value=1.99, P<0.05). Conclusion The computer-aided detection for the ultrasound detection of benign and malignant breast tumors is proved to be effective and can provide useful reference for the differentiation and diagnosis of breast tumors.
Last Update: 2020-04-02