Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion
Medical image analysis is a critical job for clinicians and radiologists to attain minute insights for proper diagnosis. The presence of complementary details of the region of interest (ROI) from multiple medical imaging modalities instigates the researchers to integrate or combine the pathological...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9864156/ |
| _version_ | 1818494438643597312 |
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| author | Vijayarajan Rajangam Dheeraj Kandikattu Utkarsh Mukul Kumar Alex Noel Joseph Raj |
| author_facet | Vijayarajan Rajangam Dheeraj Kandikattu Utkarsh Mukul Kumar Alex Noel Joseph Raj |
| author_sort | Vijayarajan Rajangam |
| collection | DOAJ |
| description | Medical image analysis is a critical job for clinicians and radiologists to attain minute insights for proper diagnosis. The presence of complementary details of the region of interest (ROI) from multiple medical imaging modalities instigates the researchers to integrate or combine the pathological details for the ease of clinical diagnosis. In this paper, the objective is to obtain a comprehensive image that presents composite image details from the two multimodal images of the same ROI. The basic idea is to generate robust fusion weights in the form of individually weighted matrices that could potentially superintend the fused outcome from the input image matrices. The extraction of texture features comes into play with the employment of the fast gray level co-occurrence matrix-mean technique. The feature maps of the source images are derived from the convolution layers on which the texture analysis is done to evaluate a weight map. Linear weights-based spatial domain fusion is employed using the weight map. Post auditioning several relevant fusion strategies and baseline hyper-parameter tuning, the obtained sets of outputs are validated via objective analysis in terms of standard metrics and compared with other fusion methods. |
| first_indexed | 2024-12-10T18:06:22Z |
| format | Article |
| id | doaj.art-9c9259ed48bf478e9ddde8874fbd618f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T18:06:22Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9c9259ed48bf478e9ddde8874fbd618f2022-12-22T01:38:37ZengIEEEIEEE Access2169-35362022-01-0110887878879710.1109/ACCESS.2022.32007529864156Texture Aware Deep Feature Map Based Linear Weighted Medical Image FusionVijayarajan Rajangam0https://orcid.org/0000-0003-0562-4472Dheeraj Kandikattu1 Utkarsh2Mukul Kumar3Alex Noel Joseph Raj4https://orcid.org/0000-0003-1505-3159Vellore Institute of Technology, Centre for Healthcare Advancement, Innovation and Research, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, IndiaDepartment of Electronic Engineering, College of Engineering, Shantou University, Shantou, ChinaMedical image analysis is a critical job for clinicians and radiologists to attain minute insights for proper diagnosis. The presence of complementary details of the region of interest (ROI) from multiple medical imaging modalities instigates the researchers to integrate or combine the pathological details for the ease of clinical diagnosis. In this paper, the objective is to obtain a comprehensive image that presents composite image details from the two multimodal images of the same ROI. The basic idea is to generate robust fusion weights in the form of individually weighted matrices that could potentially superintend the fused outcome from the input image matrices. The extraction of texture features comes into play with the employment of the fast gray level co-occurrence matrix-mean technique. The feature maps of the source images are derived from the convolution layers on which the texture analysis is done to evaluate a weight map. Linear weights-based spatial domain fusion is employed using the weight map. Post auditioning several relevant fusion strategies and baseline hyper-parameter tuning, the obtained sets of outputs are validated via objective analysis in terms of standard metrics and compared with other fusion methods.https://ieeexplore.ieee.org/document/9864156/Feature mapGLCMmedical image fusiontexture mapdeep learning |
| spellingShingle | Vijayarajan Rajangam Dheeraj Kandikattu Utkarsh Mukul Kumar Alex Noel Joseph Raj Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion IEEE Access Feature map GLCM medical image fusion texture map deep learning |
| title | Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion |
| title_full | Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion |
| title_fullStr | Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion |
| title_full_unstemmed | Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion |
| title_short | Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion |
| title_sort | texture aware deep feature map based linear weighted medical image fusion |
| topic | Feature map GLCM medical image fusion texture map deep learning |
| url | https://ieeexplore.ieee.org/document/9864156/ |
| work_keys_str_mv | AT vijayarajanrajangam textureawaredeepfeaturemapbasedlinearweightedmedicalimagefusion AT dheerajkandikattu textureawaredeepfeaturemapbasedlinearweightedmedicalimagefusion AT utkarsh textureawaredeepfeaturemapbasedlinearweightedmedicalimagefusion AT mukulkumar textureawaredeepfeaturemapbasedlinearweightedmedicalimagefusion AT alexnoeljosephraj textureawaredeepfeaturemapbasedlinearweightedmedicalimagefusion |