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

Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for an efficient clinical analysis. This paper presents a two-stage multimodal fusion framework using the cascaded combination of stationary wavelet transform (SWT) and non sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality, and phase information in the finally fused image. The first stage employs a principal component analysis algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance the contrast of the diagnostic features. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results.

Multimodal Medical Image Sensor Fusion Framework Using Cascade of Wavelet and Contourlet Transform Domains

The field of multimodal medical image fusion has made huge progress in the past decade. However, previous methods always suffer from color distortion , blurring , and noise . To address these problems, we propose a novel Laplacian redecomposition (LRD) framework tailored to multimodal medical image fusion in this article. The proposed LRD has two technical innovations. First, we present a Laplacian decision graph decomposition scheme with image enhancement to obtain complementary information, redundant information, and low-frequency subband images. Second, considering the heterogeneous characteristics of redundant and complementary information, we introduce the concept of the overlapping domain (OD) and non-OD (NOD), where the OD contributes to fuse redundant information while the NOD is responsible for fusing complementary information. In addition, an inverse redecomposition scheme is given by leveraging the global decision graph and local mean to reconstruct high-frequency subband fusion images. Finally, the inverse Laplacian transform is applied to generate the fusion result. Experimental results demonstrate that the proposal outperforms other current popular fusion methods qualitatively and quantitatively.

Laplacian Redecomposition for Multimodal Medical Image Fusion

Multimodal medical image fusion review: Theoretical background and recent advances

An image fusion based on multimodal medical images renders a considerable enhancement in the quality of fused images. An effective image fusion technique produces output images by preserving all the viable and prominent information gathered from the source images without any introduction of flaws or unnecessary distortions. This review paper intends to bring out the process of image fusion, its utilization in the medical domain, merits, and demerits and reviews the perspective of multimodal medical image fusion. It also discusses the involvement of various medical entities like medical resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). The usefulness of such modalities is presented, suggesting plausible hybrid modality combinations which could greatly enhance image fusion. This review also discusses innovative dispositions in the medical image fusion techniques for the achievement of incisively desired, quality images focused on fusion with wavelet transform and use of independent component analysis (ICA) and principal component analysis (PCA) techniques for the purpose denoising and data dimension reductions. Additionally, the future-prospects of an ideal technique for medical image fusion through the utilization of various medical modalities have been also discussed in this review paper. Graphical abstract.

Image fusion using hybrid methods in multimodality medical images

Fusion of various images aids the rejuvenation of complementary attributes of the images. Similarly, medical image fusion constructs a composite image comprehending significant traits from multimodal source images. Current work exhibits medical image fusion utilizing Laplacian Pyramid (LP) employing DCT. LP decomposes the source medical images as different low pass filtered images, resembling a pyramidal structure. As the pyramidal level of decomposition increases, the quality of the fused image also increases. The proposed technique provides a fused image with better edges and information content from human visual system (HVS) point of view. Qualitative and quantitative analysis of the proposed technique is found to be superior than that of Daubechies complex wavelet transform (DCxWT).

Medical image fusion with Laplacian Pyramids

Medical image fusion for merging of complementary diagnostic content has been carried out in this paper using Principal Component Analysis (PCA) and Wavelets. The proposed fusion approach involves sub-band decomposition using 2D-Discrete Wavelet Transform (DWT) in order to preserve both spectral and spatial information. Further, PCA is applied on the decomposed coefficients to maximize the spatial resolution. An optimal variant of the daubechies wavelet family has been selected experimentally for better fusion results. Simulation results demonstrate an improvement in visual quality of the fused image in comparison to other state-of-art fusion approaches.

Medical image fusion using combination of PCA and wavelet analysis

Curvelet transform achieves a compact representation of edges and curved shapes in the image, which other techniques like wavelets and ridgelets are not able to represent. This property of curvelet transform facilitates the retrieval of complementary information from medical images for precise and efficient clinical diagnosis. This paper presents a combination of curvelet transform along with principal component analysis (PCA) and maximum selection rule as an improved fusion approach for MRI and CT-scan. The proposed fusion approach involves image decomposition using curvelet transform followed by application of PCA for dimensionality reduction and the selection of maximum matrix to select only the relevant information in the images. Fusion factor (FF) and structural similarity index (SSIM) are used for performance evaluation of the proposed approach. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics.

Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule

Medical image fusion facilitates the retrieval of complementary information from medical images and has been employed diversely for computer-aided diagnosis of diseases. This paper presents a combination of Principal Component Analysis (PCA) and ridgelet transform as an improved fusion approach for MRI and CT-scan. The proposed fusion approach involves image decomposition using 2D-Ridgelet transform in order to achieve a compact representation of linear singularities. This is followed by application of PCA as a fusion rule to improve upon the spatial resolution. Fusion Factor (FF) and Structural Similarity Index (SSIM) are used as fusion metrics for performance evaluation of the proposed approach. Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics.

PCA Based Medical Image Fusion in Ridgelet Domain

Medical image fusion facilitates the retrieval of complementary information from medical images for diagnostic purposes This paper presents a combination of Principal Component Analysis (PCA) and Dual Tree Complex Wavelet (DTCWT) as an improved fusion approach for MR and CT-scan images Unlike real valued discrete wavelet transforms, DTCWT provides shift invariance and improved directionality along with preservation of spectral content The decomposed images are then processed using PCA a based fusion rule to improve upon the resolution and reduce the redundancy Simulation results demonstrate an improvement in visual quality of the fused image supported by higher values of fusion metrics; this further justifies the effectiveness of the proposed approach in comparison to other approaches

Himanshi

Papers

Medical image fusion with Laplacian Pyramids

Medical image fusion using combination of PCA and wavelet analysis

Medical Image Fusion in Curvelet Domain Employing PCA and Maximum Selection Rule

PCA Based Medical Image Fusion in Ridgelet Domain

An improved medical image fusion approach using PCA and complex wavelets