CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis

Abstract

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

Multi-view classification with convolutional neural networks

“…Within the past years, several collection-based datasets were acquired by researchers or citizen-science and community-driven initiatives enabling us to investigate multi-view classification on large real-world datasets from three different domains: (1) plants [ 10 ], (2) car brands and models [ 11 ], and (3) insects (the Formicidae ants genera) [ 5 ]. With emerging consumer-level technologies, e.g., 3D scanning and visual odometry from image series [ 12 – 15 ], we expect an increasing number of collection-based datasets and rising interest in multi-view classification.…”

“…(1) plants [10], (2) car brands and models [11], and (3) insects (the Formicidae ants genera) [5]. With emerging consumer-level technologies, e.g., 3D scanning and visual odometry from image series [12][13][14][15], we expect an increasing number of collection-based datasets and rising interest in multi-view classification.…”

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

“…The enhancement of input image contrast is an important and useful step to improve an image's visual quality [44][45][46]. The primary motivation of this step is to visualize the COVID-19-positive images with more clarity.…”

Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients

“…This is the hot topic in the application area of pattern recognition. Finally, the Convolutional Sparse Image Decomposition (CSID) fusion method [ 46 ] is used to concatenate the feature vectors selected in the matrix to acquire a feature vector for the classification. The final fusion is computed in Equation ( 21 ) as: …”