Multi-focal image fusion technique using convolutional neural network

Abstract

In this paper, a solution to the problems faced by different images such as multifocal and medical images is found through a simulation process using brain magnetic resonance imaging (MRI) to make a fuse based on previously approved fusion techniques such as convolutional neural networks (CNN). An algorithm is developed with the introduction of the Euclidean distance algorithm as part of the processes to make the implementation faster and more efficient than the traditional CNN. The objective fusion metrics that are commonly used are implementing to make a quantitative evaluation. The proposed system consists of three main phases which are, pre-processing phase, features extraction phase, and classification phase. The preprocessing phase is used to enhance the images by using the techniques of digital image processing. Feature extraction phase is used to get features from medical images based on the concept of Histogram of Orientation Gradient (HOG) technique feature that applied to the medical image after conversion using mean filter, adaptive filter, Discrete Wavelet Transform (DWT), k-means clustering Singular Value Decomposition (k-SVD).