Segmentation of Medical Serial Images Based on K-means and GVF Model

The medical CT images are irregular and have deep boundary concavities. So how to get the organ picture from serial images quickly and accurately is a difficult process. The paper discusses the shortcoming of GVF model being sus- ceptible to structures with slender topology. For the better convergence we improve GVF model by setting the initial con- tour as the actual contour. The new algorithm combines k-means cluster with GVF model. Firstly, the target organ is ex- tracted from a CT slice through k-means cluster and morphological reconstruction, and then its edge is set as an initial contour of the adjacent CT sequence, finally, the organ is segmented from a sequence of images with GVF algorithm. The process is repeated until all slices from entire CT sequences are obtained. The new algorithm has higher segmentation ac- curacy and lower complexity.