Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multi-modal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. This review article provides a factual listing of methods and summarizes the broad scientific challenges faced in the field of medical image fusion. We characterize the medical image fusion research based on (1) the widely used image fusion methods, (2) imaging modalities, and (3) imaging of organs that are under study. This review concludes that even though there exists several open ended technological and scientific challenges, the fusion of medical images has proved to be useful for advancing the clinical reliability of using medical imaging for medical diagnostics and analysis, and is a scientific discipline that has the potential to significantly grow in the coming years.

Medical Image Fusion: A survey of the state of the art

Medical image fusion: A survey of the state of the art

An overview of multi-modal medical image fusion

Advances in Multimodal Data Fusion in Neuroimaging: Overview, Challenges, and Novel Orientation

A novel wavelet-based approach for medical image fusion is presented, which is developed by taking into not only account the characteristics of human visual system (HVS) but also the physical meaning of the wavelet coefficients. After the medical images to be fused are decomposed by the wavelet transform, different-fusion schemes for combining the coefficients are proposed: coefficients in low-frequency band are selected with a visibility-based scheme, and coefficients in high-frequency bands are selected with a variance based method. To overcome the presence of noise and guarantee the homogeneity of the fused image, all the coefficients are subsequently performed by a window-based consistency verification process. The fused image is finally constructed by the inverse wavelet transform with all composite coefficients. To quantitatively evaluate and prove the performance of the proposed method, series of experiments and comparisons with some existing fusion methods are carried out in the paper. Experimental results on simulated and real medical images indicate that the proposed method is effective and can get satisfactory fusion results.

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Medical image fusion via an effective wavelet-based approach

Prediction of Type 2 Diabetes using Machine Learning Classification Methods

Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms

Breast Cancer Prediction using varying Parameters of Machine Learning Models

Assessment of Anxiety, Depression and Stress using Machine Learning Models

In this paper, a region level based image fusion technique, using wavelet transform, has been implemented and analyzed. The proposed methodology considers regions as the basic feature for representing images and uses region properties for extracting the information from them. A segmentation algorithm is proposed for extracting the regions in an effective way for fusing the images. The fusion strategy uses multi-level decomposition of the images obtained using wavelet transform. By analyzing the images at multiple levels, the proposed method is able to extract finer details from them and in turn improves the quality of the fused image. The performance and relative importance of the proposed methodology is investigated using the mutual information criteria. Experimental results show that the proposed method improves the quality of the fused image significantly for both the normal and multifocused images

Shruti Garg

Papers

Prediction of Type 2 Diabetes using Machine Learning Classification Methods

Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms

Breast Cancer Prediction using varying Parameters of Machine Learning Models

Assessment of Anxiety, Depression and Stress using Machine Learning Models

Multilevel Medical Image Fusion using Segmented Image by Level Set Evolution with Region Competition