Multi-feature Extraction and Classification of Breast Tumor in Ultrasound Image.
10.3969/j.issn.1671-7104.2020.04.003
- Author:
Li REN
1
;
Yangyang LIU
2
;
Ying TONG
2
;
Xuehong CAO
1
;
Yiyun WU
3
Author Information
1. School of Communication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003.
2. School of Information and Communication Engineering, Nanjing Institute of Technology, Nanjing, 211167.
3. Nanjing University of Chinese Medicine, Nanjing, 210029.
- Publication Type:Journal Article
- Keywords:
boundary feature;
breast tumor;
classification;
shape feature;
texture feature
- MeSH:
Algorithms;
Breast Neoplasms;
diagnostic imaging;
Humans;
Support Vector Machine;
Ultrasonography
- From:
Chinese Journal of Medical Instrumentation
2020;44(4):294-301
- CountryChina
- Language:Chinese
-
Abstract:
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%.
CONCLUSIONS:The classification accuracy of boundary features was higher than that of texture features and shape features. The classification method based on multi-features has the highest accuracy and it describes the benign and malignant tumors from different angles. The research results have practical value.
