A Survey on Advanced Image Processing Techniques for Telemedicine and Smart Healthcare Systems

Abstract

Telemedicine and smart healthcare systems have emerged as transformative solutions to bridge the gap between medical expertise and underserved populations, particularly in remote or rural areas. At the heart of this transformation lies medical image processing, which aids in early diagnosis, effective monitoring, and timely treatment of diseases. This survey paper investigates the latest advancements in image processing techniques, with a particular focus on segmentation, shape analysis, texture analysis, compression, and fusion of multimodal medical images. In the diagnosis of brain tumors and other critical ailments, accurate segmentation helps distinguish between benign and malignant growths, while shape and texture descriptors enhance diagnostic confidence. Image compression, especially lossless techniques, facilitates secure and efficient transmission of medical data in telemedicine environments. Furthermore, image fusion integrates complementary information from multiple imaging modalities like MRI, CT, PET, and SPECT, offering a holistic view of the patient's condition. By referencing state-of-the-art methods published in IEEE, Springer, and Elsevier journals from 2024 and 2025, this paper offers a comprehensive overview of research trends and identifies challenges in the domain. The study also explores how these techniques contribute to the development of real-time, hardware-integrated smart healthcare systems, paving the way for more accessible and effective clinical services through telemedicine.

Country : India

1 Praneel Kumar Peruru2 Kasa Madhavi

  1. Research Scholar, Dept. of CSE, JNTUACEA, JNTUA, Ananthapuramu, India
  2. Professor, Department of Computer Science and Engineering, JNTUACEA, JNTUA, Ananthapuramu, India

IRJIET, Volume 9, Special Issue of ICCIS-2025 May 2025 pp. 61-69

doi.org/10.47001/IRJIET/2025.ICCIS-202509

References

  1. T. Shaik, X. Tao, L. Li, H. Xie, and J. D. Velásquez, “A survey of multimodal information fusion for smart healthcare: Mapping the journey from data to wisdom,” Information Fusion, vol. 101, 2024. [Online]. Available: https://doi.org/10.1016/j.inffus.2023.102040.
  2. V. Lokesha and M. Veera, “An efficient medical image compression technique for telemedicine systems,” International Journal of Applied Engineering Research, vol. 10, no. 55, pp. 383–386, 2015.
  3. J. Kaur and S. Kaur, “Medical image compression: A review,” International Journal of Engineering Research and Applications, vol. 3, no. 4, pp. 937–941, 2013.
  4. G. Bhatnagar and Q. M. J. Wu, “A new contrast based multimodal medical image fusion framework,” Neurocomputing, vol. 157, pp. 143–152, 2015.
  5. M. K. Kalaiselvi and R. Arulmozhi, “Multimodal medical image fusion using NSCT and modified SFLA,” International Journal of Biomedical Engineering and Technology, vol. 24, no. 3, pp. 257–274, 2017.
  6. X. Chen, H. Xie, and B. Lei, “Artificial intelligence and multimodal data fusion for smart healthcare: Topic modeling and bibliometrics,” Artificial Intelligence Review, vol. 57, 2024.
  7. K. Ma, Z. Zhang, Y. Gao, and Y. Zhang, “Multi-modal medical image fusion based on convolutional neural network,” IEEE Access, vol. 7, pp. 8833–8845, 2019.
  8. H. Liu, Z. Yang, and X. Guo, “Multimodal medical image fusion based on joint sparse representation and dictionary learning,” Biomedical Signal Processing and Control, vol. 65, 2021.
  9. C. Wang, Z. Dong, and J. Yang, “Deep learning-based fusion method for multimodal medical images,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–12, 2021.
  10. K. M. Rao and S. B. Aruna, “Medical image compression using improved arithmetic coding with transform domain,” Procedia Computer Science, vol. 167, pp. 2162–2171, 2020.
  11. R. M. Rao and S. L. Venkatesh, “Efficient telemedicine framework for low-bandwidth environments using hybrid image compression,” Telemedicine and e-Health, vol. 26, no. 5, pp. 592–599, 2020.
  12. Y. Zhou et al., “Patch-based texture feature extraction towards improved clinical applications,” Computers in Biology and Medicine, vol. 166, 2024.
  13. N. Desai and P. V. Shah, “Shape and texture feature extraction techniques for classification of medical images: A survey,” Procedia Computer Science, vol. 132, pp. 122–129, 2018.
  14. M. M. Rahman, A. A. S. Alhassan, and M. S. Hossain, “Shape and texture-based classification of medical images using support vector machine,” Healthcare Technology Letters, vol. 6, no. 2, pp. 42–47, 2019.
  15. J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2015, pp. 3431–3440.
  16. C. N. Lakhotia et al., “Survey of Medical Image Compression Techniques,” Elsevier Computers in Biology and Medicine, vol. 132, pp. 104315, 2021.
  17. S. Saha, “Lossless and Lossy Compression Techniques for Medical Images,” Journal of Digital Imaging, vol. 27, no. 2, pp. 233–244, 2022.
  18. G. K. Wallace, “The JPEG Still Picture Compression Standard,” IEEE Transactions on Consumer Electronics, vol. 38, no. 1, pp. xviii–xxxiv, 1992.
  19. D. Taubman and M. Marcellin, “JPEG2000: Image Compression Fundamentals, Standards and Practice,” Springer, 2012.
  20. A.R. Meena and K. Raja, “Medical Image Compression using Hybrid DWT and SPIHT Techniques,” Springer Health and Technology, vol. 10, no. 4, pp. 925–936, 2023.
  21. B. E. Usefi and J. Ghasemi, “Fractal Image Compression in Medical Imaging,” IEEE Transactions on Image Processing, vol. 31, pp. 1743–1752, 2022.
  22. X. Liu et al., “Medical Image Compression Using Deep Convolutional Autoencoders,” IEEE Access, vol. 11, pp. 75563–75574, 2023.
  23. L. Wang and Z. Zhang, “End-to-End Neural Image Compression for Medical Applications,” Neurocomputing, vol. 502, pp. 245–257, 2023.
  24. S. K. Mitra and M. K. Mandal, “Evaluation Criteria for Diagnostic-Grade Image Compression,” SPIE Medical Imaging, vol. 117, pp. 321–327, 2021.
  25. M. A. Khan et al., “Secure Medical Image Compression Using Deep Encryption and Wavelet Fusion,” Elsevier Computer Methods and Programs in Biomedicine, vol. 231, pp. 107284, 2023.
  26. S. T. Acton, “Texture and Shape Analysis for Biomedical Image Classification,” IEEE Reviews in Biomedical Engineering, vol. 15, pp. 202–218, 2022.
  27. B. Van Ginneken et al., “Feature Extraction in Medical Imaging: A Review,” Medical Image Analysis, vol. 73, pp. 102157, 2021.
  28. C. Zhang and J. Liu, “Shape-Based Feature Extraction Using Fourier Descriptors,” Elsevier Pattern Recognition Letters, vol. 142, pp. 35–42, 2021.
  29. P. Mokhtarian and A. Mackworth, “A Theory of Multiscale Curvature Descriptors,” CVGIP: Image Understanding, vol. 51, no. 3, pp. 283–303, 1990.
  30. R. Gonzalez and R. Woods, Digital Image Processing, Pearson, 4th ed., 2018.
  31. A.C. Bovik, “Handbook of Image and Video Processing,” Academic Press, 2nd ed., 2005.
  32. T. R. Chandran et al., “Zernike Moments Based Shape Features for Histopathology Image Classification,” Springer Health and Technology, vol. 11, no. 2, pp. 241–251, 2023.
  33. M. K. Hu, “Visual Pattern Recognition by Moment Invariants,” IRE Transactions on Information Theory, vol. 8, no. 2, pp. 179–187, 1962.
  34. R. M. Haralick et al., “Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.
  35. G. Thibault et al., “Texture Indexes and Gray Level Size Zone Matrix: Application to Cell Classification,” Elsevier Pattern Recognition, vol. 46, no. 3, pp. 824–837, 2013.
  36. T. Ojala et al., “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
  37. A.M. Pons et al., “Fractal Texture Analysis of Medical Images: A Review,” Elsevier Computers in Biology and Medicine, vol. 139, pp. 105005, 2021.
  38. S. Z. Li, Markov Random Field Modeling in Image Analysis, Springer, 3rd ed., 2009.
  39. M. Unser, “Texture Classification and Segmentation Using Wavelet Frames,” IEEE Transactions on Image Processing, vol. 4, no. 11, pp. 1549–1560, 1995.
  40. D. Dunn et al., “Texture Segmentation Using 2-D Gabor Elementary Functions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, no. 2, pp. 130–149, 1994.
  41. S. A. Hussein et al., “Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning,” Springer MICCAI, pp. 249–258, 2017.
  42. Z. Li et al., “Deep Learning-Based Feature Extraction for Histopathological Image Analysis,” IEEE Reviews in Biomedical Engineering, vol. 14, pp. 1–13, 2021.