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An integration of meta-heuristic approach utilizing kernel principal component analysis for multimodal medical image registration

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Abstract

Medical image registration is vital for precise healthcare diagnosis, treatment planning, and disease progression tracking, but traditional methods fail to capture complex spatial transformations and anatomical variations. A Kernel Principal Component Analysis (KPCA) driven Teaching Learning based optimization (TLBO) approach is proposed to overcome these limitations. The proposed approach is categorized into three phases, i.e., pre-processed phase, contour extraction phase with feature extraction using KPCA, and evaluating robust affine transformation parameters leveraging TLBO for accurate alignment. In the pre-processing phase, gaussian filter is applied to remove noise from source and target images, followed by normalization process. Afterwards, contour extraction is carried out to create a feature image that represents the boundaries of an image. Centroid localization is then utilized to compute translation parameters, which determine the spatial alignment between the images. By utilizing KPCA, this method captures non-linear relationships in the data to enhance the representation of image features. TLBO is employed to optimize the rigid transformation parameters to improve the accurate alignment of source and target images. Extensive experiments are carried out on monomodal and multimodal medical images such as CT and MRI taken from the Harvard Brain ATLAS, Kaggle as well as in-house clinical dataset to demonstrate the effectiveness of the proposed approach. The proposed approach significantly outperforms state-of-the-art methods, with improvements of 44.37% in Root Mean Square Error (RMSE), 19.93% in Structural Similarity Index Measure (SSIM), 20.01% in Peak Signal-to-Noise Ratio (PSNR), and 16.21% in Cross Correlation (CC) quality assessment metrics.

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Data availability

The datasets used for the experimental work in the present study are considered from the publicly available datasets “Harvard Atlas Brain dataset [10]–[15]” and real time clinically dataset taken from the Fortis Hospital Mohali under the supervision of Dr. Harsimrat Bir Singh Sodhi, who holds degrees in MBBS, MS - General Surgery, and MCh - Neuro Surgery, specializing in neurosurgery upon reasonable request.

References

  1. Jiang, X., Ma, J., Xiao, G., Shao, Z., Guo, X.: A review of multimodal image matching: Methods and applications. Information Fusion 73(2020), 22–71 (2020). https://doi.org/10.1016/j.inffus.2021.02.012

    Article  Google Scholar 

  2. Nandi, D., Ashour, A.S., Samanta, S., Chakraborty, S., Salem, M.A.M., Dey, N.: Principal component analysis in medical image processing: A study. Int. J. Image Min. 1(1), 65–86 (2015). https://doi.org/10.1504/ijim.2015.070024

    Article  Google Scholar 

  3. Hu, J., et al.: Towards Accurate and Robust Multi-modal Medical Image Registration using Contrastive Metric Learning. IEEE Access. 7, 132816–132827 (2019). https://doi.org/10.1109/ACCESS.2019.2938858

    Article  Google Scholar 

  4. Arora, P., Mehta, R., Ahuja, R.: An adaptive medical image registration using hybridization of teaching learning-based optimization with affine and speeded up robust features with projective transformation. Cluster Comput. (2023). https://doi.org/10.1007/s10586-023-03974-3

    Article  Google Scholar 

  5. Leng, C., Xiao, J., Li, M., Zhang, H.: Robust Adaptive Principal Component Analysis Based on Intergraph Matrix for Medical Image Registration,. Computational Intelligence and Neuroscience 2015(1), 55–65 (2015). https://doi.org/10.1155/2015/829528

    Article  Google Scholar 

  6. Vishwakarma, H., Katiyar, S.K.: Accuracy assessment of projective transformation based hybrid approach for automatic satellite image registration. Int. J. Civil Eng. Technol. 9(13), 1514–1523 (2018)

    Google Scholar 

  7. Shehanaz, S., Daniel, E., Guntur, S.R., Satrasupalli, S.: Optimum weighted multimodal medical image fusion using particle swarm optimization. Optik. 231(1), 166413 (2021). https://doi.org/10.1016/j.ijleo.2021.166413

    Article  ADS  Google Scholar 

  8. Saoji, S.U., Sarode, M.V.: Speckle and rician noise removal from medical images and Ultrasound images. Int. J. Recent Technol. Eng. 8(5), 1851–1854 (2020). https://doi.org/10.35940/ijrte.e5993.018520

    Article  Google Scholar 

  9. Handa, B., Singh, G., Kamal, R., Oinam, A.S., Kumar, V.: Evaluation method for the optimization of 3D rigid image registration on multimodal image datasets. Int. J. Eng. Adv. Technol. 9(1), 5539–5545 (2019). https://doi.org/10.35940/ijeat.A2078.109119

    Article  Google Scholar 

  10. Keith, A., Johnson, Becker, J.A.: The Whole Brain ATLAS. Harvard University. https://www.med.harvard.edu/aanlib/home.html (2008). Accessed July 2023

  11. VBOOKSHELF: Brain CT Images with Intracranial Hemorrhage Masks. https://www.kaggle.com/vbookshelf/computed-tomography-ct-images (2019). Accessed August 2023

  12. Feltrin, F.: Brain Tumor MRI images 44 classes. Kaggle. https://www.kaggle.com/datasets/fernando2rad/brain-tumor-mri-images-44c (2022). Accessed August 2023

  13. Sartaj: Brain Tumor Classification (MRI). Kaggle  https://www.kaggle.com/datasets/sartajbhuvaji/brain-tumor-classification-mri (2019). Accessed July 2023

  14. Simeon, A.: Brain Tumor Images Dataset. Kaggle https://www.kaggle.com/datasets/simeondee/brain-tumor-images-dataset  (2019). Accessed August 2023

  15. Chakrabarty, N.: Brain MRI Images for Brain Tumor Detection. Kaggle https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection (2019). Accessed August 2023

  16. Shang, L., Lv, J.C., Yi, Z.: Rigid medical image registration using PCA neural network. Neurocomputing. 69, 13–15 (2006). https://doi.org/10.1016/j.neucom.2006.01.007

    Article  Google Scholar 

  17. Sadruddin, S., Ali, R.: Use of wavelet-fuzzy features with PCA for Image Registration. BVICA M’s International Journal of Information Technology. 6(1), 672–676 (2014)

    Google Scholar 

  18. Reel, P.S., Dooley, L.S., Wong, P.: Efficient image registration using fast principal component analysis. 19th IEEE International Conference on Image Processing 1, 1661–1664 (2012). https://doi.org/10.1109/ICIP.2012.6467196

    Article  Google Scholar 

  19. Hazra, J., Chowdhury, A.R., Dasgupta, K., Dutta, P.: A hybrid structural feature extraction-based intelligent predictive approach for image registration. Innov. Syst. Softw. Eng. (2022). https://doi.org/10.1007/s11334-022-00436-8

    Article  Google Scholar 

  20. Jin, J., Bi, X., Jiang, M., Chang, J., Cui, J.: Medical image registration based on PCA and M _ PSNR. J. Complex. Health Sci. 3(1), 62–72 (2020). https://doi.org/10.21595/chs.2020.21266

    Article  Google Scholar 

  21. Reel, P.S., Dooley, L.S., Wong, K.C.P.: Multimodal retinal image registration using a fast principal component analysis hybrid based similarity measure. IEEE International Conference on Image Processing 9, 1428–1432 (2013). https://doi.org/10.1109/ICIP.2013.6738293

    Article  Google Scholar 

  22. Xu, A., Jin, X., Guo, P., Bie, R.: KICA feature extraction in application to FNN based Image Registration. IEEE Int. Joint Conf. Neural Netw. Proc. no 7, 3602–3608 (2006). https://doi.org/10.1109/IJCNN.2006.247371

    Article  Google Scholar 

  23. Duan, X., Tian, Z., Ding, M., Zhao, W.: Registration of remote-sensing images using robust weighted kernel principal component analysis. AEUE - International Journal of Electronics and Communications. 67(1), 20–28 (2013). https://doi.org/10.1016/j.aeue.2012.05.011

    Article  Google Scholar 

  24. Chen, Y., Lin, C.: PCA based regional mutual information for robust medical image registration. IEICE Technical Report. 109 (65),  355–362 (2011). [Online]. Available: https://doi.org/10.1007/978-3-642-21111-9_40

  25. Garg, S., Ahuja, R., Singh, R., Perl, I.: GMM-LSTM: A component driven resource utilization prediction model leveraging LSTM and gaussian mixture model. Cluster Comput. 26(6), 3547–3563 (2023). https://doi.org/10.1007/s10586-022-03747-4

    Article  Google Scholar 

  26. Nazir, I., Haq, I., Alqahtani, S.A., Jadoon, M.M., Dahshan, M.: Machine Learning-Based Lung Cancer Detection Using Multiview Image Registration and Fusion. Journal of Sensors 2023, 6683438 (2023)

    Article  Google Scholar 

  27. Huizinga, W., et al.: PCA-based groupwise image registration for quantitative MRI. Med. Image. Anal. 29(4), 65–78 (2016). https://doi.org/10.1016/j.media.2015.12.004

    Article  CAS  PubMed  Google Scholar 

  28. Narayanan, A., Rajasekaran, M.P., Zhang, Y., Govindaraj, V., Thiyagarajan, A.: ScienceDirect multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Integr. Med. Res. 39(2), 350–381 (2018). https://doi.org/10.1016/j.bbe.2018.12.003

    Article  Google Scholar 

  29. Azam, M.A., Khan, K.B., Ahmad, M., Mazzara, M.: Multimodal Medical Image Registration and Fusion for Quality Enhancement. Computers Mater. Continua. 68(1), 821–840 (2021). https://doi.org/10.32604/cmc.2021.016131

    Article  Google Scholar 

  30. Senthilvel, V., Zhang, V.G.Y., Rajasekaran, P., Arun, M., Thiyagarajan, P.: A smartly designed automated map based clustering algorithm for the enhanced diagnosis of pathologies in brain MR images. Expert Systems (2020). https://doi.org/10.1111/exsy.12625

    Article  Google Scholar 

  31. Su, M., Zhang, C., Chen, Z., Jiang, S.: Registration of multimodal brain images based on optical flow, 10th International Congress on Image and Signal Processing. BioMedical Engineering and Informatics, CISP-BMEI 2017. 2017 (10), 1–5 (2017).

  32. Natarajan, S., Govindaraj, V., Zhang, Y.: Biomedical Signal Processing and Control minimally parametrized segmentation framework with dual metaheuristic optimisation algorithms and FCM for detection of anomalies in MR brain images. Biomed. Signal Process. Control. 78, 103866 (2022). https://doi.org/10.1016/j.bspc.2022.103866

    Article  Google Scholar 

  33. Zheng, Q., Wang, Q., Ba, X., Liu, S., Nan, J., Zhang, S.: A Medical Image Registration Method Based on Progressive Images. Computational and Mathematical Methods in Medicine 2021(7), 1–10 (2021). https://doi.org/10.1155/2021/4504306.

    Article  CAS  Google Scholar 

  34. Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: Transformer for unsupervised medical image registration. Medical image analysis (2022). https://doi.org/10.1016/j.media.2022.102615

    Article  PubMed  PubMed Central  Google Scholar 

  35. Singh Gill, H., Singh Khehra, B., Singh, A., Kaur, L.: Teaching-learning-based optimization algorithm to minimize cross entropy for selecting multilevel threshold values. Egypt. Inf. J. 20(1), 11–25 (2019). https://doi.org/10.1016/j.eij.2018.03.006

    Article  Google Scholar 

  36. Kher, H.R.: Implementation of Image Registration for Satellite Images using Mutual Information and Particle Swarm Optimization Techniques. Int. J. Comput. Appl. 97(1), 7–14 (2014). https://doi.org/10.5120/16969-5475

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Paluck Arora, Rajesh Mehta & Rohit Ahuja

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  1. Paluck Arora
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PA have made a substantial contribution to the concept or design, interpretation of data for the article. RM drafted the article for important intellectual content. RA revised the draft and approved the version for final submission.

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Correspondence to Paluck Arora.

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Arora, P., Mehta, R. & Ahuja, R. An integration of meta-heuristic approach utilizing kernel principal component analysis for multimodal medical image registration. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04281-1

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