Authors:
Delia Mitrea
1
;
Sergiu Nedevschi
1
and
Radu Badea
2
Affiliations:
1
Technical University of Cluj-Napoca, Romania
;
2
I. Hatieganu University of Medicine and Pharmacy, Romania
Keyword(s):
Complex Textural Microstructure Co-occurrence Matrices (CTMCM), Textural Model, Hepatocellular Carcinoma, Ultrasound Images, Classification Performance.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Classification
;
Computer Vision, Visualization and Computer Graphics
;
Feature Selection and Extraction
;
Image Understanding
;
Medical Imaging
;
Pattern Recognition
;
Software Engineering
;
Theory and Methods
Abstract:
The hepatocellular carcinoma is one of the most frequent malignant liver tumours. The golden standard for HCC detection is the needle biopsy, but this is a dangerous technique. We aim to perform the non-invasive recognition of this tumour, using computerized methods within ultrasound images. For this purpose, we defined the textural model of HCC, consisting of the relevant textural features that separate this tumour from other visually similar tissues and of the specific values that correspond to these relevant features: arithmetic mean, standard deviation, probability distribution. In this paper, we demonstrate the role that the Complex Textural Microstructure Co-occurrence Matrices have in the improvement of the textural model of HCC and in the increase of the recognition performance. During the experiments, we considered the following classes: cirrhosis, HCC, cirrhotic parenchyma on which HCC evolved and hemangioma, a frequent benign liver tumour. The resulted recognition accuracy
for HCC was towards 90%.
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