In order to solve the problems that the traditional pulse coupled neural network (PCNN) refers only to binary segmentation and does not work well for bigger image regions with sluggish gray variation,a multi-region image segmentation method was proposed based on PCNN. First, the initial image was preprocessed by smoothing and normalizing and put into PCNN. Then, with the help of fast linking, neurons with similar states fired synchronously to finish single region segmentation in each iteration processing. After pre-configured iterations, the total firing times of each neuron were calculated as the pixel intensity of new input image,and then preprocessed by smoothing and normalizing again,and finally put into PCNN. The above processing was repeated to complete multi-region segmentation. Experimental results on Berkeley image database showed that the proposed method was efficient, robust and could be used to segment image effectively.
