title: Automatic classification of left ventricular wall segments in small animal ultrasound imaging.
abstract: 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.
authors: Department of Mathematics and Computer Science, University of Münster, Münster, Germany.
authors: Kathrin
authors: K
authors: Ungru
authors: Department of Mathematics and Computer Science, University of Münster, Münster, Germany; Department of Cardiovascular Medicine, Division of Cardiology, University Hospital Münster, Münster, Germany.
authors: Daniel
authors: D
authors: Tenbrinck
authors: Department of Mathematics and Computer Science, University of Münster, Münster, Germany; Cluster of Excellence EXC 1003, Cells in Motion (CiM), University of Münster, Münster, Germany. Electronic address: xjiang@uni-muenster.de.
authors: Xiaoyi
authors: X
authors: Jiang
authors: Department of Cardiovascular Medicine, Division of Cardiology, University Hospital Münster, Münster, Germany.
authors: Jörg
authors: J
authors: Stypmann
mesh_terms: D000818:Animals; D001331:Automation; D006352:Heart Ventricles / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D051379:Mice; D014463:Ultrasonography
keywords: Classification; Echocardiography; Optical flow; Probabilistic relaxation; Shape prior segmentation; Tissue characterization