Abstract—In this paper, we propose a novel multimodal MRI and CT images fusion method based on Multi-resolution Singular Value Decomposition (MSVD) and Modified Pulse Coupled Neural Network (MPCNN). Firstly, the input pre-registered MRI and CT images are decomposed into high frequency (HF) and low frequency (LF) sub-bands by using the MSVD transform. Then, the MPCNN model is applied on each LF sub-bands. The proposed method can adaptively determine the linking strength of the MPCNN model. After that, LF coefficients are combined based on the output of MPCNN coefficients while HF coefficients are fused by using the maximum selection rule. Finally, the inverse MSVD is applied to reconstruct the fused image. Visual effect and objective evaluation criteria are used to evaluate the performance of our approach for nine pairs of MRI and CT images. The experimental results demonstrate that the proposed method has a better performance than other current methods.

Index Terms—Key words: Multimodal image fusion, PCNN, MSVD, link strength coefficient.

The authors are with Université de Tunis El Manar, Institut Supérieur d’Informatique, Research Team SIIVA- Laboratory. LIMTIC, 2 Rue AbouRayhaneBayrouni, Ariana 2080, Tunisia.

Cite:Hajer Ouerghi, Olfa Mourali, Ezzeddine Zagrouba, "Multimodal Medical Image Fusion Using Modified PCNN Based on Linking Strength Estimation by MSVD Transform," International Journal of Computer and Communication Engineering vol. 6, no. 3, pp. 201-211, 2017.