Vol.128, No.1, 2021, pp.203-222, doi:10.32604/cmes.2021.015159
OPEN ACCESS
ARTICLE
The Research of Automatic Classification of Ultrasound Thyroid Nodules
  • Yanling An1, Shaohai Hu1,*, Shuaiqi Liu2,3, Jie Zhao2,3,*, Yu-Dong Zhang4
1 Institute of Information Science, Beijing Jiaotong University, Beijing, 100044, China
2 College of Electronic and Information Engineering, Hebei University, Baoding, 071000, China
3 Machine Vision Technology Innovation Center of Hebei Province, Baoding, 071000, China
4 Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK
* Corresponding Authors: Shaohai Hu. Email: ; Jie Zhao. Email: zhaojie
(This article belongs to this Special Issue: Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)
Received 26 November 2020; Accepted 01 March 2021; Issue published 28 June 2021
Abstract
This paper proposes a computer-aided diagnosis system which can automatically detect thyroid nodules (TNs) and discriminate them as benign or malignant. The system firstly uses variational level set active contour with gradients and phase information to complete automatic extraction of the boundaries of thyroid nodules images. Then according to thyroid ultrasound images and clinical diagnostic criteria, a new feature extraction method based on the fusion of shape, gray and texture is explored. Due to the imbalance of thyroid sample classes, this paper introduces a weight factor to improve support vector machine, offering different classes of samples with different weights. Finally, thyroid nodules are classified and discriminated by the improved support vector machine. Experiments show that the efficiency of discrimination on benign and malignant thyroid nodules is improved.
Keywords
Thyroid nodules; active contour model; feature extraction; image classification
Cite This Article
An, Y., Hu, S., Liu, S., Zhao, J., Zhang, Y. (2021). The Research of Automatic Classification of Ultrasound Thyroid Nodules. CMES-Computer Modeling in Engineering & Sciences, 128(1), 203–222.
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