Comput Methods Programs Biomed - Automatic classification of left ventricular wall segments in small animal ultrasound imaging.

Tópicos

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Resumo

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.

Resumo Limpo

multipl statist show heart diseas one main caus mortal high develop societi today diseas lead chang physiolog heart give use inform characterist sever defect fast reliabl diagnosi base success therapi first step toward recognit heart remodel process work propos fulli automat process pipelin region classif left ventricular wall ultrasound imag small anim pipelin base stateoftheart method comput vision pattern classif myocardi wall segment motion estim featur extract use segment data realiz automat classifi imag region normal abnorm myocardi tissu perform propos pipelin evalu comparison common classif algorithm ultrasound data live mice artifici induc myocardi infarct given shown result work reach maximum accuraci encourag base investig

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