Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image
REN Li1, LIU Yangyang2, TONG Ying2, CAO Xuehong1, 2, WU Yiyun3
1 School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications,
2 School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167
3 Nanjing University of Chinese Medicine, Nanjing, 210029
Objective Feature extraction of breast tumors is very important in the breast tumor detection (benign
and malignant) in ultrasound image. The traditional quantitative description of breast tumors has
some shortcomings, such as inaccuracy. A simple and accurate feature extraction method has been
studied. Methods In this paper, a new method of boundary feature extraction was proposed. Firstly,
the shape histogram of ultrasound breast tumors was constructed. Secondly, the relevant boundary
feature factors were calculated from a local point of view, including sum of maximum curvature, sum
of maximum curvature and peak, sum of maximum curvature and standard deviation. Based on the
boundary features, shape features and texture features, the linear support vector machine classifiers
for benign and malignant breast tumor recognition was constructed. Results The accuracy of boundary
features in the benign and malignant breast tumors classification was 82.69%. The accuracy of shape
features was 73.08%. The accuracy of texture features was 63.46%. The classification accuracy of
the three fusion features was 86.54%. Conclusion The classification accuracy of boundary features
was higher than that of texture features and shape features. The classification method based on multifeatures
has the highest accuracy and it describes the benign and malignant tumors from different
angles. The research results have practical value.
REN Li1, LIU Yangyang2, TONG Ying2, CAO Xuehong1, 2, WU Yiyun3.Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image[J] Chinese Journal of Medical Instrumentation, 2020,V44(4): 294-301
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