Automatic detection of left ventricular asynergy by fuzzy reasoning

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Recently, as sophisticated medical instruments have been developed, the internal state of a human body has become well-known. However, a doctor's burden becomes heavier because the number of images which are taken per person with medical instruments drastically increases. Therefore, the development of automatic diagnostic imaging systems is needed. Incidentally, as Japanese daily life is Americanized, heart diseases, such as angina and myocardial infarction, are increasing. We need to observe consecutive cardiac muscle motion to detect their diseases. The left ventricular axis and the contact points in the heart region are defined, and then cardiac muscle momentum is extracted. We discriminate an abnormal case and a normal case by using a neural network and fuzzy reasoning to confirm the effectiveness of our approach. Finally, in order to show the effectiveness of the proposed method, we show simulation examples using real images.

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