Liver Ct Image Sequence Segmentation Based On Genetic Algorithm Applied Research

Computed tomography(CT) images have been widely used for liver disease diagnosis. Designing and developing image processing techniques to help doctors improve their diagnosis has been a research hot over the past years.Image Segmentation is a kind of image processing technique that devide an image into several subareas. Those areas are not intersectant and each has its own property. Image Segmentation is a key step in image processing. It is also a synthetic research task which is relate to medical and computer science. With the rapid development of medicine, Image segmentation is taking an important role in medical application.The detection of the liver contour is the foundation of quantitative analysis. Originally, doctor had to do it by himself from first image to the last. It was inefficient and needed a lot of time. So, how to use computer technology to extract liver contour automatically is an important research field. Many methods are used in edge extraction, but they both had disadvantages. After analysis the characteristics of CT liver serial images, using genetic algorithms(GA) to extract liver contour. It is proved to be feasible and effective.Firstly, The definition of Image Segmentation, its meaning and Trend of development are enumerated. And based on this, genetic algorithms is bring forward.Secondary, Summarized several popular medical image segmentation methods. Summarized the base theory of GA, implementing processes and its characteristics.Thirdly, According to the characteristics of serial CT liver images, GA is used in extracting the contour of Liver. Selected suitable coding mode, designed fitness function and genetic operators- reproduction, crossover and mutation.In the last part of this article, the author's ideas and advices to extend study are given.