Abstract
The fusion of multimodal images is a trending research area, especially in the field of medical image processing. The purpose of image fusion is to classify medical images efficiently. The objective of the research work is to do the fusion of multimodal medical images for doing medical image classification. In this research, a new algorithm is proposed for the detection of brain tumors based on three main steps namely, fusion, segmentation, and classification. A sparse theory-based vector selection (STVS) algorithm is proposed for image fusion. In this algorithm, the multimodal images are first converted into patches. These patches are further vectorized. The vectorized patches are employed in the creation of dictionaries. The generated dictionaries along with the vectorized patches are used for the creation of sparse matrices. From the sparse matrices, a selection vector is formed using which the fused image is generated. The segmentation of the fused image is done using Intuitionistic fuzzy set-based k-means (IFSKM) clustering and the Otsu thresholding technique. The clusters of the IFSKM are generated based on the Intuitionistic fuzzy set (IFS) scheme. Finally, classification is performed based on a DCNN architecture. The proposed system is validated using the brain images from the Harvard Medical School. Quantitative analysis reveals that the proposed scheme achieves the best performance in terms of fusion, segmentation, and classification. The proposed STVS scheme attained high values of entropy, standard deviation, PSNR in dB, mean square error (MSE), structural similarity index (SSIM), and homogeneity with the values of 7.33, 55.25, 42.85, 0.098, 64.31, and 53.52 respectively.