ROI based Lossless Medical Image Compression using Integer Wavelet Transform and Run-length Coding
Medical images of human body captured from various sources such as Computer Tomography (CT) or Magnetic Resonance Imaging (MRI) are in digital form. These imaging techniques generally produce huge amounts of data, and therefore compression of these images is a need for storage and communication purposes. A lot of progression is already done to compress these medical images which provide significant compression rates but there is a considerable loss of quality of these images. In some cases, it is necessary to maintain the quality of an image in some area of image usually called Region of Interest. In this paper, a method for obtaining compressed image is discussed using hybrid model and lossless compression technique in ROI regions. The proposed algorithm is evaluated on MRI images obtained from experimental database. Different performance metrics such as Mean Square Error, Peak Signal to Noise Ratio and Compression Ratio are used to measure the quality of images. It can be seen that proposed algorithm outperforms over other compression algorithm such as DCT, vector quantization and many more.
Keywords: Compression, Wavelets, SPIHT, Run-length Coding, Region of Interest.