A novel method of data compression using ROI for biomedical 2D images

Abstract

In the era of modern medical imaging communal, huge volume of medical image data are being acquired and need to be transmitted and archived which necessitate the development of efficient image compression techniques on both 2D and 3D images. The compression of medical images is an essential process to support remote healthcare services. The medical diagnostics through these services require more accurate information from an image. As the property of inverse proportionality between the compression rate and quality of the image takes place in any kind of compression method, there is a need to sacrifice any one of those credentials (Quality or Compression Rate). With this context, Region of Interest (ROI) codecs are emerging and reduces this proportionality that yields more compression rate without compromising the quality. In this paper, presents an ROI based near lossless image compression method that incorporates the Set Partitioning in Hierarchical Trees (SPIHT) and Vector Quantization coding for medical images.

Keywords

• Medical image
• Compression rate
• Region of interest
• Vector quantization
• Hierarchical trees
• Lossless image