Applied Research, Support Vector Machines In The Visualization Of Medical Image Segmentation

Support Vector Machines (SVM) are a kind of novel machine learning methods which have become the hotspot of machine learning because of their excellent learning performance. They also have successful applications in many fields, such as: face detection, handwriting digit recognition, text auto-categorization, etc. But as a new technique, SVM also have many shortcomings that need to be researched, including: sensitive to noise, have a limitation in the scale of training set, the shortcomings of training methods, incremental learning, and the combination with the prior knowledge, etc. The applications in many fields are limited because of these problems.A SVM based method is proposed aimed to perform the auto-segmentation of the left ventricle MRI image. The first and the most important step is to recognize and localize the target region that contains the left ventricle in the original two dimensional image with SVM. In this step an improved method is used to increase the recognize ratio. In the next step, a multi-localization thinking has been introduced to localize the edge areas, and a multi-level SVM is also introduced to improve the recognition ratio. Then within these exact regions, the gradient method is used to find the edge points and through connecting them to make out the margin of the target. Experiment result proofs the advanced method, and the auto segmentation procedures do bring the following experiments lots of convenience.