Composite Lempel - Ziv - Welch and Huffman Coding for Medical Image Compression

Abstract

Imaging in medicine plays vital role in the medicine by supporting diagnosis and treatment of a disease. DICOM images files occupy high storage space due to high resolution and quality. Medical image sharing is crucial in the healthcare sector for disease diagnosis and subsequent therapy. At present the medical imaging becomes increasingly digital and image files increase in size and require substantial storage that is accessible, resilient and easy to backup with healthcare industry standards. When image size is reduced, it makes it possible to store more images in the available memory space. To resolve this problem, it is required to reduce the size of image file by means of compressing. Various compress methods help to compress the medical image.  Compressing DICOM MRI brain images without mutating the features of the reconstructed resultant images is the main goal of this research project. The former research use technique clipped histogram equalization to compress the image. Present method produces the higher Compression Ratio and Peak Signal Noise Ratio than former method. The present research method DICOM Magnetic Resonance imaging are enhanced by Ant Colony Optimization Technique (ACO). The U-Net convolutional neural network design divided the improved image into two region such as region that important portion brain part and another part background of the MRI brain image (non-ROI). Consequent to the segmentation, the Region of interest part compacted by means of hybrid Lempel Ziv Welch (LZW) with Huffman encoding.  In LZW dictionary size is controlled by fire fly optimization method to decrease the memory space. NonROI part compressed by Embedded Zero Tree Wavelet (EZTW). At last reconstruct the image by decompression. The experimentation proves that present method with high compression ratio than the prior method.