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.