DocumentCode :
3252170
Title :
Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods
Author :
Mitrea, Delia ; Lupsor, Monica Platon ; Nedevschi, Sergiu ; Badea, Radu
Author_Institution :
Dept. of Comput.-Sci., Tech. Univ. of Cluj-Napoca, Cluj-Napoca, Romania
fYear :
2013
fDate :
2-4 July 2013
Firstpage :
633
Lastpage :
637
Abstract :
Cirrhosis characterization and grading is an important issue nowadays in the medical domain, as this disease can lead to death. We aim to discover the cirrhosis grades in a noninvasive manner, using computerized methods. Concerning the feature computation, we chose the texture-based methods, as they revealed subtle aspects of the tissue, not detectable by the human eye. For this purpose, we used first, second and third order statistics of the gray levels, edge-based statistics, statistics of the textural microstructures, and also textural features computed at multiple resolutions, after applying the Wavelet transform. All these features were inputs to clustering methods, such as k-means clustering and expectation maximization (EM), implemented for the determination of the cirrhosis grades, each grade corresponding to a certain cluster. The relevant textural features, for each discovered grade, were also identified, by computing a specific score, for each feature, based on the result of the clustering methods.
Keywords :
biomedical ultrasonics; diseases; edge detection; expectation-maximisation algorithm; eye; feature extraction; image texture; medical image processing; pattern clustering; statistics; wavelet transforms; Wavelet transform; cirrhosis characterization; cirrhosis grade determination; clustering method; disease; edge-based statistics; expectation maximization; feature computation; first order statistics; gray level; human eye; k-means clustering; medical domain; multiple resolution; second order statistics; textural feature; textural microstructure statistics; texture-based method; third order statistics; ultrasound image; Biomedical imaging; Clustering methods; Correlation; Entropy; Feature extraction; Liver; Ultrasonic imaging; Cirrhosis grades; clustering methods; relevant features; texture; ultrasound images;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Telecommunications and Signal Processing (TSP), 2013 36th International Conference on
Conference_Location :
Rome
Print_ISBN :
978-1-4799-0402-0
Type :
conf
DOI :
10.1109/TSP.2013.6614013
Filename :
6614013
Link To Document :
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