A compression algorithm for medical images and a display with the decoding function

We have developed a 3D wavelet compression algorithm for medical images that achieves a good reconstruction quality at high compression ratios. The algorithm applies a 3D wavelet transformation to a volume image set, followed by a scalar quantization and entropy coding to the wavelet coefficients. We also implemented a parallel version of the 3D compression algorithm in a local area network environment. Multiple processors on different workstations on the network are utilized to speed up the compression or decompression process. The 3D wavelet transform has been applied to 3D MR volume images and the results are compared with the results obtained using a 2D wavelet compression. Compression ratios achieved with the 3D algorithm are 40 - 90% higher than that of using the 2D compression algorithm. The results of applying parallel computing to the 3D compression algorithm indicate that the efficiency of the parallel algorithm ranges from 80 - 90%.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

As medical imaging facilities move towards film-less imaging technology, robust image compression systems are starting to play a key role. Conventional storage and transmission of large-scale raw medical image datasets can be very expensive and time-consuming. Recently, we proposed a memory-assisted lossless image compression algorithm based on Principal Component Analysis(PCA). In this paper, we further improve the performance of the algorithm in two different directions: Firstly, we replace PC A with NMF (Non Negative Matrix Factorization). NMF has several advantages in representing images with an image-like basis, results in sparse factors, and provides better user control over iterations. Secondly, we expand the single-level model with a new multilevel decomposition/projection framework to further reduce entropy of residual images. Our experimental results on X-ray images confirm that both modifications provide significant improvements over the single level PCA based algorithm as well as existing non-memory based techniques.

Block truncation coding (BTC) is a well known lossy compression scheme. Due to its low complexity and easy implementation, BTC has gained wide interest in its further development and application for image compression. Based on simple thresholding, BTC retains sharp edges and thus leads to artifacts such as the staircase effect. The second problem encountered in BTC is the splitting of homogeneous regions, which produces false contours. In this work a fuzzy approach of BTC to avoid truncating homogeneous blocks and to preserve smooth edges in two-cluster blocks is proposed. Each image block, viewed as a fuzzy set, is segmented into two clusters using a fuzzy clustering algorithm. The block is then encoded by modified fuzzy weighted means of the two clusters. Initialization strategies of the fuzzy clustering algorithm and a contextual quantization method are proposed. Experimental results show an improvement of visual quality of reconstructed images and peak signal-to-noise ratio when compared to BTC, economical BTC (EBTC), absolute moment BTC (AMBTC), and a minimum mean square error quantizer (MMSEQ). Computation time required by AMBTC, EBTC, and fuzzy BTC methods are reported.

A source coding technique for compressing HDTV (high-definition television) digital images for B-ISDN (broadband integrated services digital network) visual services is presented. The proposed technique is called hierarchical block truncation coding (HBTC). It is a combination of the block truncation coding (BTC) technique and a quadtree segmentation method. The quadtree segmentation is used to decompose an image into homogeneous regions so that the BTC method can exploit the nonstationarity of the image data. Bit rates around 1.46 b/pixel are achieved with this method. Experimental results for several HDTV images are presented. It is shown that greater image compression can be achieved by using a hierarchical BTC method in conjunction with quadtree segmentation, than by applying BTC to an image with uniform block divisions, since the correlation of uniform areas is exploited. This is especially true for appropriate images such as HDTV with large uniform areas.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

SummaryThis chapter proposes a novel color image compression technique based on Block Truncation Coding (BTC) using Cat Swarm Optimization (CSO). The original BTC method compresses a block of a grayscale image into a bitmap and a pair of quantization numbers. For a color image, which is encoded using R, G, and B channels, the BTC method enhances the compression rate using a common bitmap instead of three naturally formed bitmaps. However, it is difficult to find an efficient common bitmap due to its complexity. The CSO algorithm is generated by modeling the behaviors of cats, which show better performance in achieving the goal of finding best solution than other optimization algorithms. In this chapter, we adopt Cat Swarm Optimization (CSO) to search for a near optimal solution for this problem. The experimental results indicate that the proposed method could improve the decompressed BTC color image quality by obtaining a near optimal solution of the common bit map.

A prequantizer with the human visual effect for the DPCM

Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.

This paper proposes a novel medical image compression method using sparse representation approach that exploits the geometrical regularity of image structure. The geometric flow represents the direction in which the image gray levels have regular variations. The wavelet decomposition of geometric regularized data results in less number of significant coefficients. The directions of regularity are represented using two-dimensional vector field, and the approximation of these directions is obtained using spline representation. The directional decomposition of the image along with geometric flow is further improved by bandelet basis. The bandelet coefficients are encoded using Set Partitioning in Hierarchical Trees encoder, followed by global thresholding with fixed encoding. Experimental results demonstrate that the proposed method provides significant improvement in compression performance over state-of-the-art compression methods.

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.

The Quincunx wavelet, the lifting scheme wavelet and the second generation bandelet transform are a new method to offer an optimal representation for image geometric. In the field of medical diagnostics, interested parties have resorted increasingly to medical imaging. It is well established that the accuracy and completeness of diagnosis are initially connected with the image quality, but the quality of the image is itself dependent on a number of factors including primarily the processing that an image must undergo to enhance its quality. The quality evaluation of compressed image is necessary to judge the performance of a compression method. This paper introduces an algorithm for medical image compression based on hybrid non-subsampled contourlet (NSCT) and quincunx wavelet transforms (QWT) coupled with SPIHT coding algorithm, of which we present the objective measurements (PSNR, EDGE, WPSNR, MSSIM, VIF, and WSNR) in order to evaluate the quality of the image.

With images replacing textual and audio in most technologies, the volume of image data used in everyday life is very large. It is thus important to make the image file sizes smaller, both for storage and file transfer. Block Truncation Coding (BTC) is a lossy moment preserving quantization method for compressing digital gray level images. Even though this method retains the visual quality of the reconstructed image it shows some artifacts like staircase effect, etc. near the edges. A set of advanced BTC variants reported in literature were analyzed and it was found that though the compression efficiency is increased, the quality of the image has to be improved. An Improved Block Truncation Coding using k-means Quad Clustering (IBTC-KQ) is proposed in this paper to overcome the above mentioned drawbacks. A new approach of BTC to preserve the first order moments of homogeneous pixels in a block is presented. Each block of the input image is segmented into quad-clusters using k-means clustering algorithm so that homogeneous pixels are grouped into the same cluster. The block is then encoded by means of the pixel values in each cluster. Experimental analysis shows an improvement in the visual quality of the reconstructed image with high Peak Signal-to-Noise Ratio (PSNR) values compared to the conventional BTC and other modified BTC methods.

Many image and video compression algorithms work by splitting the image into blocks and producing variable-length code bits for each block data. If variable-length code data are transmitted consecutively over error-prone channel without any error protection technique, the receiving decoder cannot decode the stream properly. So the standard image and video compression algorithms insert some redundant information into the stream to provide some protection against channel errors. One of such redundancy is resynchronization marker, which enables the decoder to restart the decoding process from a known state in the event of transmission errors, but its frequent use should be restricted not to consume bandwidth too much. The Error Resilient Entropy Code (EREC) is well known method which can regain synchronization without any redundant information. It can work with the overall prefix codes, which many image compression methods use. This paper proposes an improvement to FEREC (Fast Error-Resilient Entropy Coding). It first calculates initial searching position according to bit lengths of consecutive blocks. Second, initial offset is decided using statistical distribution of long and short blocks, and initial offset is adjusted to insure all possible offset value can be examined. The proposed algorithm can speed up the construction of EREC slots, and can preserve compressed image quality in the event of transmission errors. The simulation result shows that the quality of transmitted image is enhanced about 0.3--3.5 dB compared with the existing FEREC when random channel error happens.

In this paper, we first propose a new embedded multilevel block truncation coding (BTC) technique. Unlike Differential pulse code modulation (DPCM), Vector quantization (VQ), and general multilevel BTC algorithms which determine the image quality at a time, the embedded multilevel BTC improves the image quality largely and progressively until obtaining an image with excellent quality. In order to reduce the bit rate efficiently, we propose a perception model and utilize it to develop a pruning algorithm. The pruning algorithm removes the useless information, which the human eyes are not sensitive, generated by the embedded multilevel BTC. The simulation results indicate that the bit rate with the proposed method is much less than that with the DPCM and general multilevel BTC under the same objective criterion, PSNR, or subjective criterion. This paper also shows that the computation complexity of the proposed method is much less than that with VQ under the same high quality reconstructed image.

Block truncation coding (BTC) is a recent technique used in the coding of image data. In the letter a modified technique for BTC coding of image data is presented which is algorithmically simple and hence easy to implement. This new technique uses only the first-order statistical information as `block overhead?. The new algorithm is shown to be optimum in the mean-square sense for a particular class of BTC algorithms. The letter presents the results of using the new algorithm for a typical image and compares the performance with that of the earlier algorithm for the same image.

Internet Learning Image compression is a useful approach this is inevitable to save any snapshots in compressed sample. The compressed image is reconstructed the usage of image retrieval system for any applications. This method guarantees garage performance in various content management packages. Mainly, Internet Learning resource environment deals with greater area complexity. The e-learning storage area complexity may be reduced with the assist of image compression strategies. There are extraordinary forms of image compression techniques invented for constructing compressed assets. Block Truncation Coding (BTC) is a kind of lossy compression technique for reducing grey scale quantities the usage of blocking and quantizing stages. This is powerful technique for compressing the photos. In the scope, the want for extra active and Generative image compression methods is important to enhance BTC principles. This work proposes notably complex and novel Generative Adversarial BTC (GA-BTC) compression models with more than one variances. GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of each GA fashions and BTC standards improve the best of block constructions and reconstructions notably. The proposed work evaluates the version complexity and efficiency using diverse Internet Learning images with exclusive compression fine measurements.