Research On The Automatic Detection Of ROI In Cardiac Sequence MRI And Segmentation Using Gibbs Random Fields

Image Segmentation is the key step to realize the research from general image processing into image analysis. It is widely used in many research fields like image recognition, image registration, image coding and so on. There comes up a lot of research in image segmentation methods in the recent years, but the following disadvantage was pointed out: Because of the lack of further analysis of prior knowledge of image data, which results in the insufficient usage of image spatial distribution information, the automatic segmentation becomes very difficult or the results of the automatic segment- ation are not ideal enough. So in this paper, some efficient improvements and special research are made in the segmentation of image regions of interest (ROJ) and texture feature. Firstly, during the process of the traditional segmentation method in cardiac MR image, in order to obtain the better effect, there must be threshold selections many times. In this paper, by training the feature threshold of the images, an efficient method for extracting and using prior knowledge is presented, which implements the cardiac segmentation automatically well. Secondly, during carrying out the unsupervised multi-texture image segment- ation, based on the estimation of traditional ML and MAP methods, we used the Gibbs Random Fields (GRF) as prior knowledge model to segment the images. The effect is almost equal to that of traditional method with previously known parameters model. Our methods proved efficient through many of the experiments. Briefly, in this paper, the further and systematic research was done at the aspects of how to build and use the prior knowledge of image and its spatial distribution information and the efficient improvement algorithm was presented.