Authors:
Delia Mitrea
1
;
Sergiu Nedevschi
1
and
Radu Badea
2
Affiliations:
1
Technical University of Cluj-Napoca, Romania
;
2
Iuliu Hatieganu University of Medicine and Pharmacy of Cluj-Napoca, Romania
Keyword(s):
Complex Extended Textural Microstructure Co-occurrence Matrix (CETMCM), Hepatocellular Carcinoma (HCC), Evolution Phases, Unsupervised Classification, Ultrasound Images.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
Abstract:
The hepatocellular carcinoma (HCC) is a frequent malignant liver tumour and one of the main causes of death. Detecting the HCC evolution phases is an important issue, aiming the early diagnosis of this tumour and patient monitoring with maximum accuracy. Our objective is to discover the evolution stages of HCC, through unsupervised classification techniques, using advanced texture analysis methods. In this work, we assessed the role that the Haralick features derived from the Complex Extended Textural Microstructure Co-occurrence Matrices (CETMCM) have in the unsupervised detection of the HCC evolution stages. A textural model for these phases was also generated. The obtained results were validated by supervised classifiers, well known for their performance, such as the Multilayer Perceptron (MLP), Support Vector Machines (SVM), respectively decision trees and they were also compared with the previously obtained results in this domain. The final classification accuracy was about 90%.