Simona Moldovanu* and Luminita Moraru
People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease. Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in a moving window of size 3x3 to enhance the texture features and to create the premise of differentiation of the myocardium structures. Texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The noise sensitivity of the fuzzy c–means classifier is overcome by using the image features. The proposed method is tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies. The results established that the entropy-based features provided superior clustering results compared to homogeneity.
entropy; homogeneity; image feature; texture spectrum; fuzzy c–means
University ``Dunarea de Jos`` of Galati, Dunarea de Jos University - Faculty of Sciences and Environment Galati