Study of image fusion optimization techniques for medical applications
Image fusion is becoming increasingly important in computer vision activities because of the larger number of capture methods. The integration of different views like multi view, multi temporal view and information which is larger together termed as image fusion (IF). In image fusion the information...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-06-01
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| Series: | International Journal of Cognitive Computing in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666307422000122 |
| _version_ | 1828070057810329600 |
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| author | Pydi Kavita Daisy Rani Alli Annepu Bhujanga Rao |
| author_facet | Pydi Kavita Daisy Rani Alli Annepu Bhujanga Rao |
| author_sort | Pydi Kavita |
| collection | DOAJ |
| description | Image fusion is becoming increasingly important in computer vision activities because of the larger number of capture methods. The integration of different views like multi view, multi temporal view and information which is larger together termed as image fusion (IF). In image fusion the information of multiple-sensors are converted into a unified image while maintaining the fidelity of critical characteristics.Healthcare image fusion procedures are used to improve picture quality by achieving the conspicuous characteristics in the fusion findings. As a result, they increase the practical usefulness of medical pictures for issue assessment and identification. Fusion of medical images are generally evaluated using the modalities like MRI-Magnetic Resonance Imaging, MRA-Magnetic Resonance Angiogram, PET-Positron Emission Tomography, SPET-Structural Positron Emission Tomography,CT-Computed Tomography, and SPECT-Single-Photon Emission Computed Tomography. Neural network and optimization techniques help in improving the quality of fused image. In this paper various fusion techniques are studied. Along with different fuson approaches, the outcomes of diverse research projects are contrasted in terms of how the pulse coupled neural network is employed and different optimization techniques. The Pulse Couple Neural Networks (PCNN) using various optimization techniques are compared. Among which the swarming mechanism of salps improves the performance of the system. The PCNN is combined with SSO algorithm and evaluated the results.From the results it is shown that PCNN- Salp Swarm Optimization (SSO)ishaving good value of Peak Signal to Noise Ratio (PSNR) with 45.93 and Structural Similarity Index(SSIM) is 0.996. |
| first_indexed | 2024-04-11T00:29:41Z |
| format | Article |
| id | doaj.art-972aa2de26da43eea4b1cc83959a24df |
| institution | Directory Open Access Journal |
| issn | 2666-3074 |
| language | English |
| last_indexed | 2024-04-11T00:29:41Z |
| publishDate | 2022-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Cognitive Computing in Engineering |
| spelling | doaj.art-972aa2de26da43eea4b1cc83959a24df2023-01-08T04:15:01ZengKeAi Communications Co., Ltd.International Journal of Cognitive Computing in Engineering2666-30742022-06-013136143Study of image fusion optimization techniques for medical applicationsPydi Kavita0Daisy Rani Alli1Annepu Bhujanga Rao2Corresponding author.; Department of Instrument technology, Andra University College of Enigineering(A), Andra University, Visakhapatnam, A.P., IndiaDepartment of Instrument technology, Andra University College of Enigineering(A), Andra University, Visakhapatnam, A.P., IndiaDepartment of Instrument technology, Andra University College of Enigineering(A), Andra University, Visakhapatnam, A.P., IndiaImage fusion is becoming increasingly important in computer vision activities because of the larger number of capture methods. The integration of different views like multi view, multi temporal view and information which is larger together termed as image fusion (IF). In image fusion the information of multiple-sensors are converted into a unified image while maintaining the fidelity of critical characteristics.Healthcare image fusion procedures are used to improve picture quality by achieving the conspicuous characteristics in the fusion findings. As a result, they increase the practical usefulness of medical pictures for issue assessment and identification. Fusion of medical images are generally evaluated using the modalities like MRI-Magnetic Resonance Imaging, MRA-Magnetic Resonance Angiogram, PET-Positron Emission Tomography, SPET-Structural Positron Emission Tomography,CT-Computed Tomography, and SPECT-Single-Photon Emission Computed Tomography. Neural network and optimization techniques help in improving the quality of fused image. In this paper various fusion techniques are studied. Along with different fuson approaches, the outcomes of diverse research projects are contrasted in terms of how the pulse coupled neural network is employed and different optimization techniques. The Pulse Couple Neural Networks (PCNN) using various optimization techniques are compared. Among which the swarming mechanism of salps improves the performance of the system. The PCNN is combined with SSO algorithm and evaluated the results.From the results it is shown that PCNN- Salp Swarm Optimization (SSO)ishaving good value of Peak Signal to Noise Ratio (PSNR) with 45.93 and Structural Similarity Index(SSIM) is 0.996.http://www.sciencedirect.com/science/article/pii/S2666307422000122Imaging modalitiesMedical image fusionMRICTPETSPECT |
| spellingShingle | Pydi Kavita Daisy Rani Alli Annepu Bhujanga Rao Study of image fusion optimization techniques for medical applications International Journal of Cognitive Computing in Engineering Imaging modalities Medical image fusion MRI CT PET SPECT |
| title | Study of image fusion optimization techniques for medical applications |
| title_full | Study of image fusion optimization techniques for medical applications |
| title_fullStr | Study of image fusion optimization techniques for medical applications |
| title_full_unstemmed | Study of image fusion optimization techniques for medical applications |
| title_short | Study of image fusion optimization techniques for medical applications |
| title_sort | study of image fusion optimization techniques for medical applications |
| topic | Imaging modalities Medical image fusion MRI CT PET SPECT |
| url | http://www.sciencedirect.com/science/article/pii/S2666307422000122 |
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