Automatic segmentation of biological tissues is a vital step in computer-aided diagnosis and pathology detection.In abdominal computed tomography (CT) images,r enal tissues have int ensity inhomogeneity and cannot be segmented accurately by the traditional C-V model.In this paper,according to the characteristics of renal tissues in CT imag es,an improved C-V model combining global and local informatio n is proposed to solve the problem,which is able to get better segmentation resu lts.At first,based on the prior knowledge of renal tissues in CT images,the regi ons of interest including renal tissues are obtained to speed up the operation.A t the same time,in the pre-processing stage,cortical propert ies are described in mathematical expressions and the renal initial contour is a lso obtained to locate the renal tissues roughly.Then,in order to improve the lo cal adaptability for extracting renal tissues,the local information is introduce in the C-V model within the contour evolution in the regions of interest,which provid es a more reliable basis for contour convergence on renal boundaries.Compared wi th the available methods,the experimental results show that the kidney segmentation of our proposed method is closer to the groun d truth and accordingly the dice coefficient of our kidney segmentation is about 94.0%.