Multiple statistics show that heart diseases are one of the main causes of mortality in our highly developed societies today. These diseases lead to a change of the physiology of the heart, which gives useful information about characteristic and severity of the defect. A fast and reliable diagnosis is the base for successful therapy. As a first step towards recognition of such heart remodeling processes, this work proposes a fully automatic processing pipeline for regional classification of the left ventricular wall in ultrasound images of small animals. The pipeline is based on state-of-the-art methods from computer vision and pattern classification. The myocardial wall is segmented and its motion is estimated. A feature extraction using the segmented data is realized to automatically classify the image regions into normal and abnormal myocardial tissue. The performance of the proposed pipeline is evaluated and a comparison of common classification algorithms on ultrasound data of living mice before and after artificially induced myocardial infarction is given. It is shown that the results of this work, reaching a maximum accuracy of 91.46%, are an encouraging base for further investigation. 
Automatic classification of left ventricular wall segments in small animal ultrasound imaging