An IoT Method for Telemedicine: Lossless Medical Image Compression Using Local Adaptive Blocks

Image compression gains prominence in picture archiving and communication systems for storage and transmission of data. The advancements in technology enable the usage of computer-assisted algorithms for disease diagnosis and therapeutic planning. In this research work, a prediction-based compression algorithm was proposed to compress DICOM images. The local adaptive block-based predictor compression technique utilizes neighborhood pixel gray values for prediction and the Huffman coder was employed for the encoding of predicted coefficients. The compression results were considered effective, in contrast with the JPEG lossless, Kernel Least Mean Square, Context Adaptive Block-Based Prediction, and Least Mean square Prediction approaches. The performance validation by metrics reveals the effectiveness of the proposed local adaptive block prediction-based approach. The proposed compression algorithm was implemented in Raspberry Pi B+ embedded processor as IoT application. The compressed images are transferred through the cloud to other nodes in the network, thus facilitating the teleradiology for disease diagnosis by physicians.