Waveatom transform-based multimodal medical image fusion

Gambhir, Deepak; Manchanda, Meenu

doi:10.1007/s11760-018-1360-3

Cited by 11 publications

(3 citation statements)

References 17 publications

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“…Furthermore, the image gradient vectors, obtained using (6) and 7, are used to find edges as, Figure 3 depicts a comparison of edge information in the source and enhanced images obtained after completion of the first two phases of our proposed CSID algorithm. Figure 3a,b present the source CT and MRI images, respectively, while their edge maps are demonstrated in Figure 3c,d, respectively.…”

Section: Edge Detectionmentioning

confidence: 99%

“…[1][2][3][4][5]. However, the latter requires greater attention as enhanced clinical diagnosis remains the top priority around the world [6]. In this regard, clinical imaging plays a vital role in modern day health care systems, where Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are among the most extensively used imaging modalities [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…These images provide anatomical statistics [7]; however, the extraction of purposeful functional details from an individual image remains a critical issue. This demands multimodal image fusion, which integrates the complementary information of images from different modalities to produce an enhanced fused image through simulation, thereby providing enriched anatomical and functional information [6,7,[11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Edge Detectionmentioning

confidence: 99%