Optimized Huffman encoding based medical image compression with Improved HDBSCAN

With the development of medical imaging amenities, a rising quantity of data emerges in the present image processing that has led to gradually more burden for data transmission and storage. Image compression is a method of lessening the excess in images and symbolizing it in a short way that could permit more gainful exploitation of storage capacity and network bandwidth. This paper develops a new image compression model with steps like segmentation, encoding, and decoding. Initially, segmentation is carried out using Improved Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). This phase assists in ROI separation. Subsequently, compression occurs using Improved Huffman encoding. Also, in particular, the encoding parameters are optimally chosen via a new algorithm named Snake Updated BES Optimization (SU-BESO). In the last phase, decoding is done, during which, Huffman decoding as well as region fusion are carried out. Finally, the examination is done to prove the potential of the developed SU-BESO model.