Multi-Modal Medical Image Fusion Using Curvelet Algorithm

Mathiyalagan, P.

doi:10.1109/icacci.2018.8554759

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…• When the image region containing a pixel's neighbourhood that is uniform, its bar graph are going to be powerfully peaked, and therefore the transformation perform can map a slender vary of constituent values to the complete vary of the resultant image [14]. This causes AHE to over amplify the little amounts of noise in for the most part uniform regions of the image [4].…”

Section: Adaptive Histogram Equalizationmentioning

confidence: 99%

“…This amalgamated image is a lot of appropriate for human visual, machine perception or additional image process and analysis tasks. Another advantage of image fusion is that it decreases the cupboard space and price by storing solely the one amalgamated image, rather than storing totally different modality pictures [14]. within the space of medical imaging, combining the photographs {of totally different| of various} modalities of same scene offers numerous benefits it should be fusion of image taken at different spatial resolution, intensity and by totally different strategies helps medical practitioner/Radiologists to simply extract or acknowledge the options or abnormalities that will not be typically visible in single image [18] (Figure 1).…”

Section: Image Fusion 31 Dwt Image Fusionmentioning

confidence: 99%

“…A reduced set is way easier to research and interpret. The foremost simple thanks to build a amalgamate image of many input pictures is playing the fusion as a weighted superposition of all input images [14]. The best coefficient coefficients, with relevancy info content and redundancy removal, is determined by a principal element analysis (PCA) of all input intensities.…”

Section: Principal Component Analysismentioning

confidence: 99%

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A Hybrid Image Fusion Algorithm for Medical Applications

Devi¹,

Borra²,

Sagar³

2021

Multimedia Information Retrieval

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The main objective of medical imaging is to get a extremely informative image for higher designation. One modality of medical image cannot offer correct and complete data in several cases. In brain medical imaging, resonance Imaging (MRI) image shows structural data of the brain with none useful information, wherever as pc imaging (CT) image describes useful data of the brain however with low spatial resolution particularly with low dose CT scan, that is helpful to scale back the radiation impact to physique. Within the field of diagnosing, Image fusion plays a really very important role. Fusing the CT and tomography pictures provides a whole data concerning each soft and exhausting tissues of the physique. This paper proposes a 2 stage hybrid fusion formula. Initial stage deals with the sweetening of a coffee dose CT scan image exploitation totally different image sweetening techniques viz., bar graph Equalization and adaptation bar graph deed. Within the second stage, the improved low dose CT scan image is united with tomography image exploitation totally different fusion algorithms viz., distinct rippling rework (DWT) and Principal element Analysis (PCA). The projected formula has been evaluated and compared exploitation totally different quality metrics.

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Section: Adaptive Histogram Equalizationmentioning

confidence: 99%

Section: Image Fusion 31 Dwt Image Fusionmentioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

See 1 more Smart Citation

A Hybrid Image Fusion Algorithm for Medical Applications

Devi¹,

Borra²,

Sagar³

2021

Multimedia Information Retrieval

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PEGylated and functionalized polylactide-based nanocapsules: An overview

Oliveira

Araújo

Mosqueira

2023

International Journal of Pharmaceutics

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A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion

Lin,

Chen,

Feng

et al. 2024

Sci Rep

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Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer from poor image quality and loss of crucial details due to inadequate handling of semantic information and limited feature extraction capabilities. This paper introduces a novel medical image fusion technique leveraging unsupervised image segmentation to enhance the semantic understanding of the fusion process. The proposed method, named DUSMIF, employs a multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms to refine the feature extraction and fusion processes. An innovative approach that utilizes unsupervised image segmentation to extract semantic information is introduced, which is then integrated into the fusion process. This not only enhances the semantic relevance of the fused images but also improves the overall fusion quality. The paper proposes a sophisticated network structure that extracts and fuses features at multiple scales and across multiple branches. This structure is designed to capture a comprehensive range of image details and contextual information, significantly improving the fusion outcomes. Multiple attention mechanisms are incorporated to selectively emphasize important features and integrate them effectively across different modalities and scales. This approach ensures that the fused images maintain high quality and detail fidelity. A joint loss function combining content loss, structural similarity loss, and semantic loss is formulated. This function not only guides the network in preserving image brightness and texture but also ensures that the fused image closely resembles the source images in both content and structure. The proposed method demonstrates superior performance over existing fusion techniques in objective assessments and subjective evaluations, confirming its effectiveness in enhancing the diagnostic utility of fused medical images.

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Multi-Modal Medical Image Fusion Using Curvelet Algorithm

Cited by 8 publications

References 11 publications

A Hybrid Image Fusion Algorithm for Medical Applications

A Hybrid Image Fusion Algorithm for Medical Applications

PEGylated and functionalized polylactide-based nanocapsules: An overview

A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion

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