Classification of liver diseases based on ultrasound image texture features

Sendren Sheng Dong Xu, Chun Chao Chang, Chien Tien Su, Pham Quoc Phu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This paper discusses using computer-aided diagnosis (CAD) to distinguish between hepatocellular carcinoma (HCC), i.e., the most common type of primary liver malignancy and a leading cause of death in people with cirrhosis worldwide, and liver abscess based on ultrasound image texture features and a support vector machine (SVM) classifier. Among 79 cases of liver diseases including 44 cases of liver cancer and 35 cases of liver abscess, this research extracts 96 features including 52 features of the gray-level co-occurrence matrix (GLCM) and 44 features of the gray-level run-length matrix (GLRLM) from the regions of interest (ROIs) in ultrasound images. Three feature selection models-(i) sequential forward selection (SFS), (ii) sequential backward selection (SBS), and (iii) F-score-are adopted to distinguish the two liver diseases. Finally, the developed system can classify liver cancer and liver abscess by SVM with an accuracy of 88.875%. The proposed methods for CAD can provide diagnostic assistance while distinguishing these two types of liver lesions.

Original languageEnglish
Article number342
JournalApplied Sciences (Switzerland)
Volume9
Issue number2
DOIs
Publication statusPublished - Jan 19 2019

Keywords

  • Classification
  • F-score
  • Gray-level co-occurrence matrix (GLCM)
  • Gray-level run-length matrix (GLRLM)
  • Hepatocellular carcinoma (HCC)
  • Image texture
  • Liver abscess
  • Liver cancer
  • Sequential backward selection (SBS)
  • Sequential forward selection (SFS)
  • Support vector machine (SVM)
  • Ultrasound images

ASJC Scopus subject areas

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Classification of liver diseases based on ultrasound image texture features'. Together they form a unique fingerprint.

Cite this