Neuro-fuzzy logic based fusion algorithm of medical images

Performance assessment of neuro fuzzy based image fusion of satellite images

“…Suggested a recent CNN-based multi-focus image fusion approach that displays the capability of CNNs for other-type image fusion subjects. Also locates advancing some proposals on the upcoming training of CNN-based image fusion [6].…”

“…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].…”

A Survey on Multimodal Medical Image Fusion

“…This makes the neural network attractive to image fusion as the nature of variability between the images is subjected to change every time a new modality is used. The ability to train the neural network to adopt to these changes enable several applications for medical image fusion such as solving the problems of feature generation [36], classification [36], data fusion [36,19,27], image fusion [37,38,27,39,40,41,42,43], micro-calcification diagnosis [19], breast cancer detection [38,44,45], medical diagnosis [27,28,42], cancer diagnosis [46], natural computing methods [87] and classifier fusion [45]. Although ANN offers generality in terms of having the ability to apply the concept of training, the robustness of ANN methods is limited by the quality of the training data and the accuracy of convergence of the training algorithm.…”

A New Image Fusion Algorithm Based on Wavelet Transform and Adaptive Neuro Fuzzy Logic Approach

“…The input images may be from different sensors [9], medical images [11,16], remote sensing images [7]. Image fusion aims at aggregating two or multiple images from same information sources, so as to achieve improved accuracy and robust inference performance by utilizing redundancy and complementariness in information.…”

“…This is achieved by applying a sequence of operators on the images that would make the good information in each of the image prominent [20]. The resultant image is formed by combining such magnified information from the input images into a single image.The input images may be from different sensors [9], medical images [11,16], remote sensing images [7]. Image fusion aims at aggregating two or multiple images from same information sources, so as to achieve improved accuracy and robust inference performance by utilizing redundancy and complementariness in information.…”