Classification of Liver Diseases Based on Ultrasound Image Texture Features

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...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2019-01, Vol.9 (2), p.342
Main Authors: Xu, Sendren Sheng-Dong, Chang, Chun-Chao, Su, Chien-Tien, Phu, Pham Quoc
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Biopsy
Classification
Discriminant analysis
F-score
gray-level co-occurrence matrix (GLCM)
gray-level run-length matrix (GLRLM)
hepatocellular carcinoma (HCC)
Image classification
image texture
Liver
liver abscess
Liver cancer
Liver cirrhosis
Liver diseases
Machine learning
Medical imaging
Methods
Neural networks
NMR
Nuclear magnetic resonance
Radiation
sequential backward selection (SBS)
sequential forward selection (SFS)
support vector machine (SVM)
Ultrasonic imaging
Ultrasonic testing
Ultrasound
ultrasound images
Wavelet transforms
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9020342