To improve the efficiency and accuracy of medica l image segmentation and provide more fully effective basis for clinical diagnos is and adjunctive therapy, a medical image segmentation method based on line int ercept histogram reciprocal cross entropy is proposed. Firstly, the line interce pt histogram is defined. Then, the line intercept histogram of the medical image is built considering its two-dimensional information. Finally, the reciprocal cross entropy criterion for threshold selection based on the line intercept histo gram is derived, according to which, the medical image is segmented. A large num ber of experimental results show that, compared with other methods, including two-dimensional reciprocal entropy method based on niche chaos particle swarm optimization (NCPSO), two-dim ensional exponent gray entropy method based on decomposition, symmetric cross en tropy method based on two dimensional histogram oblique segmentation, two dimens ional Tsallis cross entropy method based on particle swarm optimization (PSO) an d so on, the proposed method has superior image segmentation performance. In its segmentation result, object region is complete and accurate, and the edge details a re clear and richer. Moreover, the running time is greatly reduced. It is a fast and effective new segmentation method which can be used in medical image research.