A Novel Bacterial Foraging Optimization Based Multimodal Medical Image Fusion Approach

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Published: Oct 4, 2023
DOI: https://doi.org/10.14416/j.asep.2023.03.004
Keywords:
Medical image fusion Imaging modality Bacterial foraging optimization Discrete wavelet transform Fusion rule generation

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Gurusigaamani Ayyanar Muthulingam
Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India
Velmurugan Subbiah Parvathy
Department of Electronics and Communication Engineering, Kalasalingam Academy of Research and Education, Tamilnadu, India

Abstract

Multimodal medical image fusion (MIF) is the procedure of integrating different images in single into multiple imaging modalities for increasing the image quality by preserving a certain feature. Medical image combination covered a tremendous count of hot topic areas, involving pattern recognition, image processing, artificial intelligence (AI), computer vision (CV), and machine learning (ML). In addition, MIF was more commonly applied in clinical for physicians to understand the lesion by the combination of various modalities of medicinal image. This article introduces a novel bacterial foraging optimization-based multimodal medical image fusion approach (BFO-M3IFA). The presented BFO-M3IFA technique considered two distinct patterns of the images as the input of systems and the outcome will be the fused image. Primarily, the BFO-M3IFA technique exploits Weiner filtering (WF) technique as an image pre-processing step to get rid of the noise. Besides, discrete wavelet transform (DWT) was applied for decomposing the image into distinct subbands. Afterward, the estimated coefficients of modality 1 and comprehensive coefficients of modality 2 are integrated and vice versa. At last, a fusion rule is generated to fuse the details of two image modalities and the optimal fusion rule parameter is chosen with utilize of BFO algorithm. The experimental validation of the BFO-M3IFA system was tested and outcomes ensured the improved performance of the BFO-M3IFA system on existing models.

Article Details

How to Cite
Muthulingam, G. A., & Parvathy, V. S. (2023). A Novel Bacterial Foraging Optimization Based Multimodal Medical Image Fusion Approach. Applied Science and Engineering Progress, 16(4), 6794. https://doi.org/10.14416/j.asep.2023.03.004
Issue
Vol. 16 No. 4 (2023)
Section
Research Articles

References

B. Huang, F. Yang, M. Yin, X. Mo, and C. Zhong, “A review of multimodal medical image fusion techniques,” Computational and Mathematical Methods in Medicine, vol. 2020, pp. 1–16, Apr. 2020, doi:10.1155/2020/8279342.

B. Rajalingam and R. Priya, “Multimodality medical image fusion based on hybrid fusion techniques,” International Journal of Engineering and Manufacturing Science, vol. 7, no. 1, pp. 22–29, Nov. 2017.

T. Tirupal, B. C. Mohan, and S. S. Kumar, “Multimodal medical image fusion techniques– A review,” Current Signal Transduction Therapy, vol. 16, no. 2, pp. 142–163, Feb. 2020, doi: 10.2 174/1574362415666200226103116.

M. A. Azam, K. B. Khan, S. Salahuddin, E. Rehman, S. A. Khan, M. A. Khan, S. Kadry, and A. H. Gandomi, “A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics,” Computers in Biology and Medicine, vol. 144, May 2022, Art. no. 105253, doi: 10.1016/j. compbiomed.2022.105253.

Y. Li, J. Zhao, Z. Lv, and J. Li, “Medical image fusion method by deep learning,” International Journal of Cognitive Computing in Engineering, vol. 2, pp. 21–29, Jun. 2021, doi:10.1016/j.ijcce. 2020.12.004.

W. Tan, P. Tiwari, H. M. Pandey, C. Moreira, and A. K. Jaiswal, “Multimodal medical image fusion algorithm in the era of big data,” Neural Computing and Applications, pp. 1–21, Jul. 2020, doi: 10.1007/s00521-020-05173-2.

S. P. Yadav and S. Yadav, “Image fusion using hybrid methods in multimodality medical images,” Medical & Biological Engineering & Computing, vol. 58, pp. 669–687, Jan. 2020, doi: 10.1007/ s11517-020-02136-6.

B. Rajalingam and R. Priya, “Multimodal medical image fusion using various hybrid fusion techniques for clinical treatment analysis,” Smart Construction Research, vol. 2, no. 4, 2018, doi: 10.18686/scr.v2i4.594.

K. B. Khan, A. A. Khaliq, M. Shahid, and H. Ullah, “Poisson noise reduction in scintigraphic images using gradient adaptive trimmed meanfilter,” in 2016 International Conference on Intelligent Systems Engineering (ICISE), 2016, pp. 301–305, doi: 10.1109/INTELSE.2016.7475138.

K. B. Khan, M. Shahid, H. Ullah, E. Rehman, and M. M. Khan, “Adaptive trimmed mean autoregressive model for reduction of Poisson noise in scintigraphic images,” IIUM Engineering Journal, vol. 19, no. 2, pp. 68–79, 2018.

K. B. Khan, A. A. Khaliq, M. Shahid, and J. A. Shah, “A new approach of weighted gradient filter for denoising of medical images in the presence of Poisson noise,” Tehnicki Vjesnik - Technical Gazette, vol. 23, no. 6, pp. 1755–1762, 2016.

M. A. Azam, K. B. Khan, S. Salahuddin, E. Rehman, S. A. Khan, M. A. Khan, S. Kadry, A. H. Gandomi, “A review on multimodal medical image fusion: Compendious analysis of medical modalities, multimodal databases, fusion techniques and quality metrics,” Computers in Biology and Medicine, vol. 144, Art. no. 105253, 2022.

Z. Zhu, X. He, G. Qi, Y. Li, B. Cong, and Y. Liu, “Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI,” Information Fusion, vol. 91, pp. 376–387, 2023.

S. Goyal, V. Singh, A. Rani, and N. Yadav, “Multimodal image fusion and denoising in NSCT domain using CNN and FOTGV,” Biomedical Signal Processing and Control, vol. 71, Jan. 2022, Art. no. 103214, doi: 10.1016/j.bspc.2021.103214.

H. Hermessi, O. Mourali, and E. Zagrouba, “Multimodal medical image fusion review: Theoretical background and recent advances,” Signal Processing, vol. 183, Jun. 2021, Art. no. 108036, doi: 10.1016/j.sigpro.2021.108036.

R. Wang, N. Fang, Y. He, Y. Li, W. Cao, and H. Wang, “Multi-modal medical image fusion based on geometric algebra discrete cosine transform,” Advances in Applied Clifford Algebras, vol. 32, no. 2, pp. 1–23, Feb. 2022, doi: 10.1007/s00006- 021-01197-6.

N. Alseelawi, H. T. Hazim, and H. T. S. Alrikabi, “A novel method of multimodal medical image fusion based on hybrid approach of NSCT and DTCWT,” International Journal of Online & Biomedical Engineering, vol. 18, no. 03, Mar. 2022, doi: 10.3991/ijoe.v18i03.28011.

W. Kong, Q. Miao, R. Liu, Y. Lei, J. Cui, and Q. Xie, ”Multimodal medical image fusion using gradient domain guided filter random walk and side window filtering in framelet domain,” Information Sciences, vol. 585, pp. 418–440, 2022.

S. Shehanaz, E. Daniel, S. R. Guntur, and S. Satrasupalli, “Optimum weighted multimodal medical image fusion using particle swarm optimization,” Optik, vol. 231, Art. no. 166413, Apr. 2021, doi: 10.1016/j.ijleo.2021.166413.

B. Rajalingam and R. Priya, “Multimodal medical image fusion based on deep learning neural network for clinical treatment analysis,” International Journal of ChemTech Research, vol. 11, no. 6, pp. 160–176, May 2018.

M. A. Azam, K. B. Khan, E. Rehman, and S. U. Khan, “Smoke removal and image enhancement of laparoscopic images by an artificial multiexposure image fusion method,” Soft Computing, vol. 26, pp. 8003–8015, 2022.

M. A. Azam, K. B. Khan, M. Ahmad, and M. Mazzara, “Multimodal medical image registration and fusion for quality Enhancement,” Computers, Materials & Continua, vol. 68, no. 1, pp. 821–840, 2021.

C. Panigrahy, A. Seal, and N. K. Mahato, “MRI and SPECT image fusion using a weighted parameter adaptive dual channel PCNN,” IEEE Signal Processing Letters, vol. 27, pp. 690–694, 2020.

A. Sengupta, A. Seal, C. Panigrahy, O. Krejcar, and A. Yazidi, “Edge information based image fusion metrics using fractional order differentiation and sigmoidal functions,” IEEE Access, vol. 8, pp. 88385–88398, 2020.

A. Seal, D. Bhattacharjee, M. Nasipuri, D. Rodríguez - Esparragón, E. Menasalvas, and C. Gonzalo-Martin, “PET-CT image fusion using random forest and à-trous wavelet transform,” International Journal for Numerical Methods in Biomedical Engineering, vol. 34, no. 3, Art. no. e2933, 2018, doi: 10.1002/cnm.2933.

C. V. Cannistraci, F. M. Montevecchi, and M. Alessio, “Median‐modified Wiener filter provides efficient denoising, preserving spot edge and morphology in 2‐DE image processing,” Proteomics, vol. 9, no. 21, pp. 4908–4919, Nov. 2009, doi: 10.1002/pmic.200800538.

S. Lahmiri and M. Boukadoum, “Hybrid discrete wavelet transform and gabor filter banks processing for features extraction from biomedical images,” Journal of Medical Engineering, vol. 2013, Apr. 2013, doi: 10.1155/2013/104684.

Z. Li, Y. Qian, H. Wang, X. Zhou, G. Sheng, and X. Jiang, “Partial discharge fault diagnosis based on zernike moment and improved bacterial foraging optimization algorithm,” Electric Power Systems Research, vol. 207, Jun. 2022, Art. no. 107854, doi: 10.1016/j.epsr.2022.107854.

V. S. Parvathy and S. Pothiraj, “Multi-modality medical image fusion using hybridization of binary crow search optimization,” Health Care Management Science, vol. 23, no. 4, pp. 661–669, 2020, doi: 10.1007/s10729-019-09492-2.