Hybrid Lempel–Ziv–Welch and clipped histogram equalization based medical image compression

Nowadays, the medical images upsurge because of numerous major disease predictions. The medical image size needed vast volumes of memory and taking additional bandwidth for storage as well as transmission. With the aim of decreasing the size of the storage and as well for greater transmission image compression is needed. The prior research presented a completely automatic technique for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks (CNNs), which is skilled end to-end and does not depend on past acquittance of the data. On the other hand it contains problem with storage for the period of the medical image transmission and transmission speed. With the aim of resolving this issue the presented system developed a compression method. In this research, magnetic resonance imaging are pre-processed by means of median filter. The preprocessed image is split into region of interest (ROI) and non region of interest by means of deep fully convolutional networks with Jaccard distance. Subsequent to the ROI segmentation, the ROI edge is taken out and encrypted with Freeman chain coding. At that point the ROI part is compressed by hybrid Lempel–Ziv–Welch and clipped histogram equalization (CHE). In CHE, ideal threshold value is chosen by means of particle swarm optimization technique for enhancing the brightness maintenance. The Non ROI part is compressed by means of enhanced zero tree wavelet (EZW). In this EZW technique, a preliminary threshold is chosen by making use of firefly algorithm. Lastly the decompression is carried out at the receiving end. The experimentation outcomes prove that the presented method attains greater performance when matched up with the previous technique in regard to compression ratio, peak signal to noise ratio and mean square error.