Efficient Medical Image Fusion Using 2-Dimensional Double Density Wavelet Transform to Improve Quality Metrics

Abstract

In the recent past, a medical 2D image fusion approach has played a vital role in the biomedical industry for diagnosing disease using various modalities of medical images. Biomedical 2D image fusion synthesis is a combining technique that merges useful information from two or more modalities of data captured through high definition scanning devices. The fused image is highly useful when doctors make emergency surgical decisions for their clinical patients. In this research, an innovative medical 2D image fusion approach called 2-Dimensional Double Density Wavelet Transform (2D-DDWT), based on maximization of scaling rule, is being proposed for the combination of CT and MRI images of human brain. The source image is applied to 2D-DDWT pursued by the fusion of sub images of coefficients based on high and low frequencies that are decomposed by transformation technique. The low frequency information is decomposed using the congruency rule of phase while the high frequency information is decomposed using 2D Gabor filter. The 2D-DDWT frequency coefficients are fused by using Principle Component Analysis (PCA) for the approximation parameters, and therefore the rule of selection maximum is being applied for the coefficients to improve features of image fusion such as segmentation. Maximization scaling rule is proposed for pixel level fusion to improve quality metrics. The quantitative and qualitative analysis proves the efficiency of the proposed methodology and demonstrates the improvement of the proposed methodology over existing methods.