Multimodal Medical Image Registration and Fusion for Quality Enhancement

For the last two decades, physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body. However, most of the time, medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information. To overcome this problem, a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages. In the proposed method, a Multi-resolution Rigid Registration (MRR) technique is used for multimodal image registration while Discrete Wavelet Transform (DWT) along with Principal Component Averaging (PCAv) is utilized for image fusion. The proposed MRR method provides more accurate results as compared with Single Rigid Registration (SRR), while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time. The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset. The fusion results of the proposed method are compared with the existing fusion techniques. The quality assessment metrics such as Mutual Information (MI), Normalize Cross-correlation (NCC) and Feature Mutual Information (FMI) are computed for statistical comparison of the proposed method. The proposed methodology provides more accurate results, better image quality and valuable information for medical diagnoses.

Automated summary

Over the past two decades, the use of a multimodal approach in medical imaging has proven to be effective in increasing both qualitative and quantitative information for accurate disease diagnosis. The integration of Multi-resolution Rigid Registration technique and Discrete Wavelet Transform with Principal Component Averaging in image fusion has shown to produce more accurate results and valuable medical information in a shorter computational time. The proposed method, tested on CT and MRI brain imaging modalities, offers improved image quality and enhanced medical diagnoses compared to existing techniques.