Automatic detection of left ventricular asynergy by fuzzy reasoning

Recently, as sophisticated medical instruments have I been developed, the inside state of a human body becomes well-known. However doctor's burden becomes heavier because the number of images which are taken with medical instruments per person drastically increases. Therefore, the development of an automatic diagnostic imaging systems is needed. By the way, 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. In this paper 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 the simulation examples by using real images.