To solve the problem that the regional active contour model cannot effectively segment weak targets， a regional active contour model with local entropy constraints is proposed for image segmentation. Firstly， the image is divided into two feature regions based on local entropy information. Then a local entropy binary fitting energy is constructed by using local entropy feature information， and finally a level set evolution equation is obtained， which is combined with a region-scalable fitting （RSF） model. The model considers the clustering characteristics of the gray distribution and the statistical information of the local area of the image， and it is effective in handing intensity inhomogeneity， weak edge segmentation， and flexible contour initialization. Medical image experiment results verify the effectiveness of the proposed model.