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.