An image segmentation method of a modified SPCNN based on human visual system in medical images

Abstract

An image segmentation method of a modified simplified pulse-coupled neural network (MSPCNN) based on human visual system (HVS) is proposed for medical images. The method successfully determines the stimulus input of the MSPCNN according to the characteristics of PCNN and HVS. In order to accomplish the goal, we attempt to deduce the sub-intensity range of central neurons firing by introducing neighboring firing matrix Q and calculating intensity distribution range based on a new MSPCNN(NMSPCNN), and then reveal the way how sub-intensity range parameter Sint generates the stimulus input Sioij closer to HVS. Besides, we try to substitute the above stimulus input into the MSPCNN to extract more suitable lesions for medical images. In contrast to prevalent PCNN models, the MSPCNN has higher segmentation accuracy rates and lower computational complexity because of the parameter setting method. Finally, a good proposed method comparing with the state-of-the-art methods has a better performance, presenting the overall metric OEM with MIAS of 0.8784, DDSM of 0.8606 and gallstones of 0.8585.