Perceptual lossless medical image coding

A novel perceptually lossless coder is presented for the compression of medical images. Built on the JPEG 2000 coding framework, the heart of the proposed coder is a visual pruning function, embedded with an advanced human vision model to identify and to remove visually insignificant/irrelevant information. The proposed coder offers the advantages of simplicity and modularity with bit-stream compliance. Current results have shown superior compression ratio gains over that of its information lossless counterparts without any visible distortion. In addition, a case study consisting of 31 medical experts has shown that no perceivable difference of statistical significance exists between the original images and the images compressed by the proposed coder

Lossy To Lossless Medical Image Coding Using Joint Bit Scanning Method

A new algorithm for progressive medical image coding is presented. On the 8-bit gray scale image, lifting based integer wavelet transform (IWT) are applied to get the three level multi-resolution Integer wavelet transformed image. Then, it is encoded using block based partitioning scheme to exploit the energy clustering in frequency and in space. Whenever a pixel is found significant, pixel value is completely transmitted using vertical bit scanning and then proceeds again with block based coding. Experiments are carried on MRI images to prove the effectiveness of the proposed algorithm. The results shows a significant improvement in terms of distortion measured as peak signal to noise ratio (PSNR) and Correlation Coefficient (CoC)  for a given bit rate compared to the existing state of the art embedded image coding methods

Information preserved guided scan pixel difference coding for medical images

This paper analyzes the information content of medical images, with 3-D MRI images as an example, in terms of information entropy. The results of the analysis justify the use of Pixel Difference Coding for preserving all information contained in the original pictures, lossless coding in other words. The experimental results also indicate that the compression ratio CR=2:1 can be achieved under the lossless constraints. A pratical implementation of Pixel Difference Coding which allows interactive retrieval of local ROI (Region of Interest), while maintaining the near low bound information entropy, is discussed.Comment: 5 pages and 5 figures. Published in IEEE Wescanex proceeding

FPSoC using Xilinx Zynq for medical image coding based on the quaternionic paraunitary filter banks

In this paper, we have introduced a low-cost FPSoC for medical image coding and implemented to telemedicine applications based on the Xilinx Zynq. We have recently introduced a generalized block-lifting structure using the 2-D CORDIC algorithm as a block of 4- and 8-band linear phase paraunitary filter banks (LP PUFB) based on the quaternionic algebra (Q-PUFB) with one-regularity constraints on hypercomplex coefficients of the schemes for the lossy-to-lossless image coding. Its structure can implement the integer-to-integer transform (I-Q-PUFB). The parallel-pipelined efficient architecture (P2E_Q-PUFB) has been proposed. The low latency separable image processing is implemented in the given architecture

Hierarchical Lossless Image Compression for Telemedicine Applications

AbstractThe main aim of hierarchical lossless image compression is to improve accuracy, reduce the bit rate and improve the compression efficiency for the storage and transmission of the medical images while maintain an acceptable image quality for diagnosis purpose. The cost and limitation in bandwidth of wireless channels has made compression is necessity in today's era. In medical images, the contextual region is an area which contains an important information and must be transmitted without distortion. In this paper the selected region of the image is encoded with Adaptive Multiwavelet Transform AMWT) using Multi Dimensional Layered Zero Coding (MLZC). Experimental results shows that Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), Mean Structural Similarity Index (MSSIM) performance is high and Root Mean Square Error (RMSE), Mean Absolute Error (MAE) values are low, and moderate Compression Ratio (CR) at high Bits Per Pixel (BPP) when compared to the integer wavelet and multiwavelet transform

A Progressive Universal Noiseless Coder

The authors combine pruned tree-structured vector quantization (pruned TSVQ) with Itoh's (1987) universal noiseless coder. By combining pruned TSVQ with universal noiseless coding, they benefit from the “successive approximation” capabilities of TSVQ, thereby allowing progressive transmission of images, while retaining the ability to noiselessly encode images of unknown statistics in a provably asymptotically optimal fashion. Noiseless compression results are comparable to Ziv-Lempel and arithmetic coding for both images and finely quantized Gaussian sources

Multi Spectral Band Selective Coding for Medical Image Compression

Medical image compression has recently evolved as an area of research for progressive transmission The distance based medical diagnosis demands for high quality imaging at faster data transfer rate As the information s are highly informative each pixel information defines a sample observation Hence the coding in medical diagnosis need to be of higher accuracy than conventional image coding In the approach of image coding multi spectral coding is developed as new coding approach to achieve the objective of higher visualization accuracy With this observation in this paper a multi spectral coding using multi wavelet transformation is developed The multi spectral coding is improved by a band selective approach using inter band correlation factor The evaluation factors for such a coding technique are observed to be improved over conventional multi-spectral codin

Smart-EEG: a Tele-medicine System for EEG Exams

National audienceThis paper presents Smart-EEG, a telemedicine project aiming to improve remote diagnosis of brain diseases. Those exams are commonly done by acquiring EEG, ECG, EOG, EMG and a video of the scene. Nowadays, systems uses video acquisition limited to standard frame-rate (30 fps) and unreliable synchronization mechanism. Those current limitations may lead to wrong diagnosis of a patient. To get around those limitation, the system architecture is centered around a hardware acquisition system handling synchronization and compression and a self contained EEG exam file to permit remote diagnosis. This device aims at providing reliable synchronization and high frame-rate video recordin

A Multicenter Observer Performance Study of 3D JPEG2000 Compression of Thin-Slice CT

The goal of this study was to determine the compression level at which 3D JPEG2000 compression of thin-slice CTs of the chest and abdomen–pelvis becomes visually perceptible. A secondary goal was to determine if residents in training and non-physicians are substantially different from experienced radiologists in their perception of compression-related changes. This study used multidetector computed tomography 3D datasets with 0.625–1-mm thickness slices of standard chest, abdomen, or pelvis, clipped to 12 bits. The Kakadu v5.2 JPEG2000 compression algorithm was used to compress and decompress the 80 examinations creating four sets of images: lossless, 1.5 bpp (8:1), 1 bpp (12:1), and 0.75 bpp (16:1). Two randomly selected slices from each examination were shown to observers using a flicker mode paradigm in which observers rapidly toggled between two images, the original and a compressed version, with the task of deciding whether differences between them could be detected. Six staff radiologists, four residents, and six PhDs experienced in medical imaging (from three institutions) served as observers. Overall, 77.46% of observers detected differences at 8:1, 94.75% at 12:1, and 98.59% at 16:1 compression levels. Across all compression levels, the staff radiologists noted differences 64.70% of the time, the resident’s detected differences 71.91% of the time, and the PhDs detected differences 69.95% of the time. Even mild compression is perceptible with current technology. The ability to detect differences does not equate to diagnostic differences, although perception of compression artifacts could affect diagnostic decision making and diagnostic workflow