Study And Application Based On Graph Cuts And Image Matting Theory

In recent years, there are a great deal of improvements in image segmentation,restoration and enhancement along with the introduction of graph theory and alpha matting theory. In this paper, we study on the graph cuts method in image segmentation based on graph theory and a closed-form matting algorithm. For sake of minimizing complexity of algorithm and improving the veracity of segmentation, we propose two improved models and validate the efficiency and effectivity of our methods through the natural images and medical images. The main jobs we complete are as follows:(1) Graph Cuts is a novel image segmentation method based on graph theory framework. The innovations of this theory lie in its global optimization and the unity of knowledge. However, if the image is large, computing will be very time-consuming. This paper presents a GC-based Hierarchical image segmentation method. First the initial segmentation is obtained through GC in the low-resolution with a very low computational cost. Then the contour is projected back to the high-resolution image to construct a narrow band. At last use matting arithmetic for accurate segmentation in the narrow-band. Experimental results show that this method can ensure the accuracy of segmentation results with a significant increasing in computing speed.(2) MRIs of bone tumor are vague and irregular. It's very difficult to extract the pathological tissue from the MRIs for the traditional image segmentation methods. This paper presents a new computer-aided diagnosis system of tumor MRI segmentation based on image matting technique. The system uses the solution from coarse to fine strategy, use a closed-form solution to extract the diseased tissue, simply solving a sparse linear equation to obtain the desired segmentation results. Experimental results show that this method can ensure the accuracy of segmentation results with a significant low cost of computing time. The accurate segmentation results can provide a reliable basis for clinical diagnosis for doctors.