A Neuro-Fuzzy Approach for Medical Image Fusion

Abstract

Abstract-This paper addresses a novel approach to the multisensor, multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of non-subsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN). The linking strengths of the RPCNNs' neurons are adaptively set by modelling them as the fuzzy membership values, representing their significance in the corresponding source image. Use of RPCNN with less complex structure and having less number of parameters, leads to computational efficiency, an important requirement of point-of-care (POC) health care technologies. The proposed scheme is free from the common shortcomings of the state-of-the-art MIF techniques: contrast reduction, loss of image fine details and unwanted image degradations etc. Subjective and objective evaluations show better performance of this new approach compared to existing techniques.Index Terms-Image fusion, artificial neural network, fuzzy logic, medical imaging, image analysis.

Performance assessment of neuro fuzzy based image fusion of satellite images

“…A multi-band image fusion approach ground on unsupervised spectral unmixing for collaborating high-dimensional and low-haunted determination image and a lowspatial high-spectral resolution image [5]. Suggested a recent CNN-based multi-focus image fusion approach that displays the capability of CNNs for other-type image fusion subjects.…”

“…Practice of the reduced pulse-coupled neural network (RPCNN) with a smaller amount of complex structure and having less number of parameters leads to computational efficiency and significant condition of point-of-care strength care approaches. The projected arrangement is allowed from the collective failings of the recent approaches [5]. Projected a Neuro-fuzzy technique of image fusion eliminates the spatial falsehood of wavelet based image fusion technique and incorrect boundaries as well as shady adverts [6].…”

Medical Image Fusion Algorithm based on Local Average Energy-Motivated PCNN in NSCT Domain

“…Several Image Fusion (IF) and Medical Image Fusion (MIF) techniques based on PCNN have been proposed by researchers [15][16][17][18][19][20]. The majority of the MIF techniques based on PCNN use the normalized single value of the pixel in the spatial domain or the coefficient in the transform domain as the feeding input to the PCNN which leads to contrast reduction and loss of directional information respectively [19,[21][22][23][24].…”

“…Das and Kundu [17] employed a Neuro-fuzzy approach which combines a reduced pulse coupled neural network with fuzzy logic in order to produce fused image with higher contrast, more clarity and more useful subtle detailed information. Kavitha and Chellamuthu [18] enhanced the input before feeding it into the PCNN using the ant colony optimization (ACO) technique.…”

Multimodal Medical Image Fusion Using Various Hybrid Fusion Techniques For clinical Treatment Analysis

“…Saad M. Darwish [5] proposes an image fusion system for medical engineering based on contourlet transform and multi-level fuzzy reasoning technique in which useful information from two spatially registered medical images is integrated into a new image that can be used to make clinical diagnosis and treatment more accurate. Sudeb Das, Malay Kumar Kundu [6] introduce a novel approach to the multimodal medical image fusion problem, employing multi scale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network. C. T. Kavitha, C.Chellamuthu [7] proposes image fusion based on Integer Wavelet Transform (IWT) and Neuro-Fuzzy.…”