Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images

 
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1997 (EN)
Computer assisted characterization of liver tissue using image texture analysis techniques on B-scan images (EN)

Theotokas, I (EN)
Zoumpoulis, P (EN)
Kyriacou, E (EN)
Pavlopoulos, S (EN)
Koutsouris, D (EN)

N/A (EN)

In this study, the classification of B-scan ultrasonic liver images using image texture analysis techniques is investigated. The texture analysis algorithms used were the Gray Level Difference Statistics (GLDS), the Gray Level Run Length Statistics (RUNL), the Spatial Gray Level Dependence Matrices (SGLDM) and the Fractal Dimension Texture Analysis (FDTA). All four techniques were applied on four sets of ultrasonic liver images: normal, fatty, cirrhosis and hepatoma. A total of 120 cases were investigated (30 from each class), with all abnormal cases being histologically proven. In each image, a 32×32 pixel rectangular region-of-interest was selected by an expert physician. Results were classified using the K-nearest neighbor (K-NN) classifier. The FDTA and SGLDM algorithms were able to classify the four sets with an overall accuracy of 78,3% and 77,5% respectively while the RUNL algorithm achieved 74,2% and the GLDS algorithm 70,8% overall accuracy. Combination of RUNL, SGLDM and FDTA improved the overall accuracy to 80%. (EN)

journalArticle

Fractal dimension texture analysis (EN)
Gray level run length statistics (EN)
Fractal Dimension (EN)
Medical imaging (EN)
Region of Interest (EN)
Tissue Characterization (EN)
Gray level difference statistics (EN)
Image Texture Analysis (EN)
Tissue (EN)
Image analysis (EN)
Ultrasonic imaging (EN)
Algorithms (EN)
K Nearest Neighbor (EN)
Spatial gray level dependence matrices (EN)
Texture Analysis (EN)
Ultrasound (EN)
Computer aided analysis (EN)

Εθνικό Μετσόβιο Πολυτεχνείο (EL)
National Technical University of Athens (EN)

Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings (EN)

1997


IEEE, Piscataway, NJ, United States (EN)



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