• R. Rajesh
• J. Dayalin Subi
• S.Thiruvazhi Marba Pillai

Medical image fusion is used to derive useful information from multimodality medical image data. It is used to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images. In this paper the image fusion is done using Integer Wavelet Transform (IWT) and Pulse Coupled Neural Network (PCNN), and using Fast Linking PCNN. To fuse the images using Integer Wavelet Transform (IWT) and PCNN, initially IWT of both images are applied to PCNN to get firing maps. Maximum fusion rule is then used to get the fused coefficients. Finally inverse IWT is taken to get the fused image. To fuse the images using Fast Linking PCNN, the registered CT and MRI images are applied to Fast Linking PCNN (FLPCNN). Firing maps corresponding to both the images are fused using maximum fusion rule to get the fused image. The performance of the fusion process is evaluated using entropy, standard deviation, average gradient, weighted average fusion quality index, edge dependent fusion quality index and similarity metric. The results of these methods are compared with other fusion methods.

PCNN, Unit Linked PCNN, Fast Linking PCNN

"Multimodal Medical Image Fusion using Fast Linking Pulse Coupled Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.g183-g191, October-2024, Available :http://www.jetir.org/papers/JETIR2410623.pdf

"Multimodal Medical Image Fusion using Fast Linking Pulse Coupled Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppg183-g191, October-2024, Available at : http://www.jetir.org/papers/JETIR2410623.pdf