Segmentation of serial CT images based on an improved Mumford-Shah model

Segmentation of medical image is an indispensable process in image analysis and recognition, and it provides the basis of quantitative analysis of images about human organs and functions. The Mumford-Shah model using level set method is more robust than other curve evolution models to detect discontinuities under noisy environment, which has been widely used in the field of medical image segmentation. Consequently, serial computed tomography (CT) image segmentation algorithm based on an improved Mumford-Shah model is presented. First of all, the window transformation technique of medical images is introduced, which is able to display the digital imaging and communications in medicine (DICOM) images directly and distinctly with a little information loss. Secondly, the characteristics of serial CT images as well as the topological structure relation between them are analyzed, followed by the processing method of CT image sequence, which can make the serial CT image segmentation much more automatically and swiftly. Thirdly, in the light of the problems of segmentation speed and termination in traditional Mumford-Shah model, a novel segmentation algorithm based on image entropy and simulated annealing is presented. The algorithm alleviates these two problems by using the image entropy to displace the energy coefficients in the original energy function, and also combining the simulated annealing to terminate the contours evolution automatically. Finally, the algorithm is applied in some experiments to deal with serial CT images, and the results of the experiments show that the proposed algorithm can provide a fast and reliable segmentation.