Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection
Joint Authors
Du, Yongzhao
Huang, Xiaofu
Chen, Ming
Liu, Peizhong
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Prostate cancer is one of the most common cancers in men.
Early detection of prostate cancer is the key to successful treatment.
Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer.
Although ultrasound images can show cancer lesions, subjective interpretation is not accurate.
Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours.
Firstly, the input image is preprocessed by optical density conversion.
Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed.
Finally, the fused texture features are provided to SVM classifier for classification.
The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%.
The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.
American Psychological Association (APA)
Huang, Xiaofu& Chen, Ming& Liu, Peizhong& Du, Yongzhao. 2020. Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection. Computational and Mathematical Methods in Medicine،Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1139562
Modern Language Association (MLA)
Huang, Xiaofu…[et al.]. Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection. Computational and Mathematical Methods in Medicine No. 2020 (2020), pp.1-9.
https://search.emarefa.net/detail/BIM-1139562
American Medical Association (AMA)
Huang, Xiaofu& Chen, Ming& Liu, Peizhong& Du, Yongzhao. Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection. Computational and Mathematical Methods in Medicine. 2020. Vol. 2020, no. 2020, pp.1-9.
https://search.emarefa.net/detail/BIM-1139562
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1139562