Robust multimodal fusion network employing novel Empirical Riglit Wavelet Transform for brain images

Abstract

Machine learning is useful for pattern recognition, if allowed access to patient data, it can notice patterns that would be missed by human doctors, which could be used to predict if a person is at risk for a disease that would not have been anticipated by a doctor. In this paper, the authors have proposed an Empirical Riglit Wavelet Transform algorithm. In this algorithm, the authors have fused the filter banks of CT and MR images obtained from Ridgelet and Little wood Empirical Wavelet Transform. Four possible combinations were used for the fusion. Image boundaries were evaluated as performance parameters. With that parameters helps in understanding the small elements and details from given CT and MR images. The objective of this paper is to classify and extract specific patterns in the images using different combinations of CT and MR by fusing them. The proposed algorithm is validated via filter banks obtained for fused CT-MT images using the same techniques.