A TWO COMPONENT MEDICAL IMAGE COMPRESSION TECHNIQUES FOR DICOM IMAGES

To meet the demand for high speed transmission of image, efficient image storage, remote treatment an efficient image compression technique is essential. Wavelet theory has great potential in medical image compression. Most of the commercial medical image viewers do not provide scalability in image compression. This paper discusses a medical application that contains a viewer for digital imaging and communications in medicine (DICOM) images as a core module. Progressive transmission of medical images through internet has emerged as a promising protocol for teleradiology applications. The major issue that arises in teleradiology is the difficulty of transmitting large volume of medical data with relatively low bandwidth. Recent image compression techniques have increased the viability by reducing the bandwidth requirement and cost-effective delivery of medical images for primary diagnosis. This paper presents an effective algorithm to compress and reconstruct Digital Imaging and Communications in Medicine (DICOM) images. DICOM is a standard for handling, storing, printing and transmitting information in medical imaging. These medical images are volumetric consisting of a series of sequences of slices through a given part of the body. DICOM image is first decomposed by Haar Wavelet Decomposition Method. The wavelet coefficients are encoded using Set Partitioning in Hierarchical Trees (SPIHT) algorithm. Discrete Cosine Transform (DCT) is performed on the images and the coefficients are JPEG coded. The quality of the compressed image by different method are compared and the method exhibiting highest Peak Signal to Noise Ratio (PSNR) is retained for the image. The performance of the compression of medical images using the above said technique is studied with the two component medical image compression techniques

Performance Comparison of Raster Line Difference Huffman Technique with Different Coding Techniques for Non-Medical Images

The abstract of this paper will compared so many hybrid trends compression techniques in image compression concepts with Raster Line Difference Huffman Technique for non-medical Images. In this paper we compared non medical Images like Baboon, Crane, Lena, sun and veg .In this Paper we compare size, compression rate and saving percentage of different coding techniques with Raster Line Difference Huffman Technique for non medical Images like Baboon, Crane, Lena, sun and veg . The author hope on This paper will be very helpful to known the many new hybrid techniques performance with Raster line Huffman comparison technique for non-medical images