Discovering the cirrhosis grades from ultrasound images by using textural features and clustering methods

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.