Fusion of multimodal medical images increases robustness and enhances accuracy in biomedical research and clinical diagnosis. It attracts much attention over the past decade. In this paper, an efficient multimodal medical image fusion approach based on compressive sensing is presented to fuse computed tomography (CT) and magnetic resonance imaging (MRI) images. The significant sparse coefficients of CT and MRI images are acquired via multi-scale discrete wavelet transform. A proposed weighted fusion rule is utilized to fuse the high frequency coefficients of the source medical images; while the pulse coupled neural networks (PCNN) fusion rule is exploited to fuse the low frequency coefficients. Random Gaussian matrix is used to encode and measure. The fused image is reconstructed via Compressive Sampling Matched Pursuit algorithm (CoSaMP). To show the efficiency of the proposed approach, several comparative experiments are conducted. The results reveal that the proposed approach achieves better fused image quality than the existing state-of-the-art methods. Furthermore, the novel fusion approach has the superiority of high stability, good flexibility and low time consumption.

A novel approach for multimodal medical image fusion

The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.

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Image Fusion Techniques: A Survey

A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics

Fluorescence anisotropy imaging in drug discovery.

A novel fusion scheme for visible and infrared images based on compressive sensing

Medical image fusion has been used to derive useful information from multimodality medical image data. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, so as to provide more information to the doctor and clinical treatment planning system. This paper aims to demonstrate the application of wavelet transformation to multi-modality medical image fusion. This work covers the selection of wavelet function, the use of wavelet based fusion algorithms on medical image fusion of CT and MRI, implementation of fusion rules and the fusion image quality evaluation. The fusion performance is evaluated on the basis of the root mean square error (RMSE) and peak signal to noise ratio (PSNR).

Analysis of CT and MRI Image Fusion Using Wavelet Transform

Medical image fusion has been used to derive useful information from multimodality medical image data The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, magnetic resonance imaging (MRI) provides better information on soft tissue whereas computed tomography (CT) provides better information about denser tissue Fusing these two types of images creates a composite image which is more informative than any of the input signals provided by a single modality For this reason, image fusion has become a common process used within medical diagnostics and treatment In this paper, Fast Discrete Curvelet Transform using Wrapper algorithm based image fusion technique, has been implemented, analyzed and compared with Wavelet based Fusion Technique Fusion of images taken at different resolutions, intensity and by different techniques helps physicians to extract the features that may not be normally visible in a single image by different modalities This work aims at fusion of registered CT and MRI Images This fused image can significantly benefit medical diagnosis and also the further image processing such as, visualization (colorization), segmentation, classification and computer-aided diagnosis (CAD) The fusion performance is evaluated on the basis of the root mean square error (RMSE) and peak signal to noise ratio (PSNR)

Kiran Parmar

Papers

Analysis of CT and MRI Image Fusion Using Wavelet Transform

A Comparative Analysis of Multimodality Medical Image Fusion Methods