Applications of Image Processing in Teleradiology for the Medical Data Analysis and Transfer Based on IOT
Subbiahpillai Neelakantapillai Kumar, Alfred Lenin Fred, L. R. Jonisha Miriam, Parasuraman Padmanabhan, Balázs Zoltán Gulyás, Kumar H. Ajay · 2021
Every day, a huge amount of medical images is generated for disease diagnosis and therapeutic applications. The analysis and storage of medical data is a crucial task and transfer of data is also a needy one from the perspective of telemedicine. The preprocessing, segmentation and compression algorithms gain importance in the analysis, storage and transfer of medical data. This chapter focuses on the importance of image processing techniques for disease diagnosis and detection. The medical images are corrupted by noise and an appropriate filtering algorithm is required prior to subsequent process. The medical images are stored in lossless format; however efficient lossy compression algorithms are also there for medical images. The Picture Archiving and Communication System (PACS) require an efficient compression algorithm, thereby minimizing the degradation of reconstructed image quality. The classification algorithms are used to classify the tumor stages. This chapter also discusses the hardware implementation of image processing algorithms for teleradiology applications. For preprocessing of input CT/MR images, nonlinear tensor diffusion filter was used, segmentation was done by improved FCM based on crow search optimization and compression was done by prediction-based lossless technique. The hardware implementation of algorithms was done on Raspberry Pi B+ embedded processor.