Experiments on the Feature Selection and Classiifcation of Ultrasound Elastography Images for the Diagnosis of Breast Cancers
10.3969/j.issn.1671-7104.2016.06.002
- VernacularTitle:用于乳腺癌诊断的超声弹性图像特征选择与分类实验
- Author:
Yaonan ZHANG
1
;
Yongliang ZHANG
;
Yang XIAO
Author Information
1. 西安思源学院电子信息工程学院
- Keywords:
ultrasound;
elastography images;
texture;
SVM classifier;
breast cancer
- From:
Chinese Journal of Medical Instrumentation
2016;40(6):397-402
- CountryChina
- Language:Chinese
-
Abstract:
Breast cancers are the most common malignant tumors in women, and how to use ultrasound to diagnose breast cancers quantitatively is stil an unsolved problem. This paper extracts five elastic features based on the elastography images of the breast tumors, furthers extract four features related to gray co-occurrence matrix to describe the texture of breast masses. we study the application of SVM classifier to classify these features, and uses the consistency, classification accuracy, ROC curve and AUC (area under the curve) to assess the classification results. we used ultrasound imaging technique to colect data from the actual patients, with the data of 195 lesions in 142 patients. Experimental results show that the classification performance of the elastic features is good, and the support vector machine is suitable for breast image classification, and its classification accuracy is high, which provides a good value for diagnosis. Meanwhile, it is found that the extracted features related to gray level co-occurrence matrix have a low diagnostic value.
