Version 1
: Received: 3 October 2018 / Approved: 4 October 2018 / Online: 4 October 2018 (14:01:42 CEST)
How to cite:
Xu, S.S.; Su, C.; Chang, C.; Phu, P.Q. Classification for Liver Diseases Based on Ultrasound Image Texture Features. Preprints2018, 2018100073. https://doi.org/10.20944/preprints201810.0073.v1
Xu, S.S.; Su, C.; Chang, C.; Phu, P.Q. Classification for Liver Diseases Based on Ultrasound Image Texture Features. Preprints 2018, 2018100073. https://doi.org/10.20944/preprints201810.0073.v1
Xu, S.S.; Su, C.; Chang, C.; Phu, P.Q. Classification for Liver Diseases Based on Ultrasound Image Texture Features. Preprints2018, 2018100073. https://doi.org/10.20944/preprints201810.0073.v1
APA Style
Xu, S.S., Su, C., Chang, C., & Phu, P.Q. (2018). Classification for Liver Diseases Based on Ultrasound Image Texture Features. Preprints. https://doi.org/10.20944/preprints201810.0073.v1
Chicago/Turabian Style
Xu, S.S., Chun-Chao Chang and Pham Quoc Phu. 2018 "Classification for Liver Diseases Based on Ultrasound Image Texture Features" Preprints. https://doi.org/10.20944/preprints201810.0073.v1
Abstract
This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
