Statistical Modeling of Multi-modal Medical Image Fusion Method Using C-CHMM and M-PCNN

In this paper, a new Contextual hidden Markov Model (CHMM) and modified Pulse Coupled Neural Network (M-PCNN) based fusion approach in the Contour domain is proposed for multi-modal medical image fusion. The Contour transform as an emerging multi-scale multi-direction geometric analyzing tool can provide an efficient and flexible representation of images, e.g. edges, contours and textures, which overcomes the drawback of the 2-D wavelet transform. Considering the powerful advantages for statistical modeling and processing of Contour let coefficients by HMM, the context information integrated with HMM is established to construct a comprehensive statistical correlative model, which can collectively capture persistence across scales, directional selectivity within scales and energy concentration in the spatial neighborhood of the high-frequency sub-band coefficients. Low-frequency sub-band coefficients are fused by the magnitude maximum rule, and a modified PCNN is developed where the linking strength of each neuron is determined by the normalized region energy of Edge PDF and modified spatial frequency is employed as the image feature to motivate M-PCNN. The high-frequency directional sub-band coefficients are selected by total pulse number maximum strategy. The experimental results demonstrate that the presented fusion method can further improve fusion image quality and visual effects.