<title>Three-dimensional medical image compression using a wavelet transform with parallel computing</title>

3D point cloud presentation has been widely used in computer vision, automatic driving, augmented reality, smart cities and virtual reality. 3D point cloud compression method with higher compression ratio and tiny loss is the key to improve data transportation efficiency. In this paper, we propose a new 3D point cloud geometry compression method based on deep learning, also an auto-encoder performing better than other networks in detail reconstruction. It can reach much higher compression ratio than the state-of-art while keeping tolerable loss. It also supports parallel compressing multiple models by GPU, which can improve processing efficiency greatly. The compression process is composed of two parts. Firstly, Raw data is compressed into codeword by extracting feature of raw model with encoder. Then, the codeword is further compressed with sparse coding. Decompression process is implemented in reverse order. Codeword is recovered and fed into decoder to reconstruct point cloud. Detail reconstruction ability is improved by a hierarchical structure in our decoder. Latter outputs are grown from former fuzzier outputs. In this way, details are added to former output by latter layers step by step to make a more precise prediction. We compare our method with PCL compression and Draco compression on ShapeNet40 part dataset. Our method may be the first deep learning-based point cloud compression algorithm. The experiments demonstrate it is superior to former common compression algorithms with large compression ratio, which can also reserve original shapes with tiny loss.

3D printing is becoming a more common technology and has a growing number of applications. Although 3D compression algorithms have been studied in the computer graphics (CG) community for decades, the quality of the compressed 3D models are discussed only in the CG space. In this paper, we discuss the relationship between the PSNR of the compressed 3D models and the human perception to the printed objects. We conducted subjective evaluation by inviting 13 people and found that there is a clear linear relationship between them. Such a quality perception model is useful for estimating the printing quality of the compressed 3D models and deciding reasonable compression parameters.

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.

Sketch based coding of grey level images

PurposeCone‐beam computerized tomography (CBCT) is routinely performed for verification of patient position in radiotherapy. It produced a large amount of data which require a method to compress them for efficient storage. In this study three video compression algorithms were introduced and their performance was evaluated based on real patient data.Materials and methodsAt first CBCT images in multiple sets of a patient were transferred from reconstruction workstation or exported from treatment planning system. Then CBCT images were sorted according to imaging time (time‐prioritized sequence) or imaging location (location‐prioritized sequence). Next, this sequence was processed by a video compression algorithm and resulted in a movie. Three representative video compression algorithms (Motion JPEG 2000, Motion JPEG AVI, and MPEG‐4) were employed and their compression performance was evaluated based on the CBCT data of 30 patients.ResultsAmong three video compression algorithms, Motion JPEG 2000 has the least compression ratio since it is a lossless compression algorithm. Motion JPEG AVI and MPEG‐4 have higher compression ratios than Motion JPEG 2000 but come with certain image losses. For MPEG‐4, location‐prioritized sequences show higher compression ratio than time‐prioritized sequences. Based on the results achieved on the clinical target verification application, the registration accuracy of CBCT after decompression was comparable to that of the original CBCT.ConclusionsVideo compression algorithms could provide a higher compression ratio comparing to static image compression algorithm. Although the loss of CBCT image due to compression its impact on registration accuracy of patient positioning is almost negligible. Video compression method is an effective way to substantially reduce the size of CBCT images for storage.

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.

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.

This paper presents a fast algorithm for fractal image compression. The algorithm uses quadtree partitioning to partition an image into image blocks of different sizes. Each of the image blocks is normalized to have zero mean and unity variance, and represented by a feature vector of dimension 1 6. The feature vectors, which can provide an accurate representation of the image blocks, are composed of the means and/or variances of each of the rows and columns. The k-d tree structure is used to partition the feature vectors of the domain blocks. This arrangement allows the search of the best matched domain block for a range block efficiently and accurately. An efficient encoding approach for low complexity range blocks is also proposed, which encodes the mean of a range block without searching the domain blocks. Moreover, during the range-domain matching process, a simple but very efficient search by using the property of zero contrast value is introduced, which can further improve the encoding time and compression ratio, especially in high compression ratio. This can lead to an improvement in encoding time and an increase in compression ratio, while maintaining comparable image quality. Experimental results show that the run-time required by our proposed algorithm is over 200 times faster than that of a full search.

High resolution multidimensional image data yield huge datasets. For compression and analysis, 2D approaches are often used, neglecting the information coherence in higher dimensions, which can be exploited for improved compression. We designed a wavelet compression algorithm suited for data of arbitrary dimensions, and assessed its ability for compression of 4D medical images. Basically, separable wavelet transforms are done in each dimension, followed by quantization and standard coding. Results were compared with conventional 2D wavelet. We found that in 4D heart images, this algorithm allowed high compression ratios, preserving diagnostically important image features. For similar image quality, compression ratios using the 3D/4D approaches were typically much higher (2-4 times per added dimension) than with the 2D approach. For low-resolution images created with the requirement to keep predefined key diagnostic information (contractile function of the heart), compression ratios up to 2000 could be achieved. Thus, higher-dimensional wavelet compression is feasible, and by exploitation of data coherence in higher image dimensions allows much higher compression than comparable 2D approaches. The proven applicability of this approach to multidimensional medical imaging has important implications especially for the fields of image storage and transmission and, specifically, for the emerging field of telemedicine.

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

Cache-block compression is a highly effective technique for both reducing accesses to lower levels in the memory hierarchy (cache compression) and minimizing data transfers (link compression). While many effective cache-block compression algorithms have been proposed, the design of these algorithms is largely ad hoc and manual and relies on human recognition of patterns. In this article, we take an entirely different approach. We introduce a class of “byte-select” compression algorithms, as well as an automated methodology for generating compression algorithms in this class. We argue that, based on upper bounds within the class, the study of this class of byte-select algorithms has potential to yield algorithms with better performance than existing cache-block compression algorithms. The upper bound we establish on the compression ratio is 2X that of any existing algorithm. We then offer a generalized representation of a subset of byte-select compression algorithms and search through the resulting space guided by a set of training data traces. Using this automated process, we find efficient and effective algorithms for various hardware applications. We find that the resulting algorithms exploit novel patterns that can inform future algorithm designs. The generated byte-select algorithms are evaluated against a separate set of traces and evaluations show that Byte-Select has a 23% higher compression ratio on average. While no previous algorithm performs best for all our data sets which include CPU and GPU applications, our generated algorithms do. Using an automated hardware generator for these algorithms, we show that their decompression and compression latency is one and two cycles respectively, much lower than any existing algorithm with a competitive compression ratio.

The objective of this paper is to effectively improve the existing Huffman lossless compression algorithm and to evaluate its performance on various types of medical imaging data like CT, MRI, Ultrasound, and X-ray images. Huffman Algorithm is a statistical coding technique. It is technically simple to implement for both the purposes of encoding and decoding .In this paper, a pattern finder component is proposed, which determines the best component and the most frequent occurring pattern in the image to be transmitted via the telemedicine network. The best pattern will be an input to the encoder and the output of the encoder would be the compressed image and the footer information. The footer information comprises of the data which were compressed and needs to be inserted at the decoder component. The proposed pattern replacement greatly enhances the performance in terms of improved compression ratios over the existing system. It is aptly applicable for data transfer where bandwidth should be at an optimal level. The present work yields 4-5% improved compression ratio, thereby permitting reduced traffic on the telemedicine network.KeywordsHuffman compressionPattern finderLossless compression Telemedicine

This study presents insights into the computational complexity of fractal image compression (FIC) algorithms. Unlike JPEG, a fractal encoder necessitates more CPU time in contrast to the decoder. The study examines various factors that impact the encoder and its computational cost. Many researchers have dedicated themselves to the field of fractal encoding to overcome the computational cost of the FIC algorithm. Here, this study offers a look over the approaches in the aspect of time complexity. The automated baseline fractal compression algorithm is studied to demonstrate the understanding of delay in the encoder. The study establishes how various approaches trade‐off between the quality of decoder, compression ratio, and CPU time. The experiment section shows the bargain between fidelity criteria of the baseline algorithm.

Wavelet representation of a signal is efficient for process data compression. An on‐line compression algorithm based on Haar wavelets is proposed here. As a new data point arrives, the algorithm computes all the approximation coefficients and updates the multiresolution tree before it prepares to receive the next data point. An efficient bookkeeping and indexing scheme improves compression ratio more significantly than batch‐mode wavelet compression. Reconstruction algorithms and historian format for this bookkeeping are developed. Various analytical results on the bounds on compression ratio and sum of the square error that can be achieved using this algorithm are derived. Experimental evaluation over two sets of plant data shows that wavelet compression is superior to conventional interpolative methods (such as boxcar, backward slope, and SLIM3) in terms of quality of compression measured both in time and frequency domain and that the proposed on‐line wavelet compression algorithm performs better than the batch‐mode wavelet compression algorithm due to the efficient indexing and bookkeeping scheme. The on‐line algorithm combines the high quality of compression of wavelet‐based methods and on‐line implementation of interpolative compression algorithms at the same time.

The goal of this work is to provide a brief review on the image compression algorithms in the wireless capsule endoscopy, which is efficient and simple. This paper also covers the review on each stage of the image compression algorithms and its efficiency. At the latter part of this paper, an ideal algorithm for the image compression with high compression ratio and good image reconstruction quality. The main complication associated with the wireless endoscopy system is the reduction in area and consumed power while preserving the acceptable reconstruction of video with high quality. Most of the existing algorithms are based on DCT algorithms which require more buffer space; thereby it increases the large area and large power for the computation. In this paper, the different data compressor based on DCT is presented. The ideal scheme is based on the subsampling of data. Special features and characteristics of endoscopic images are considered in the stage of algorithm development. The algorithm includes the compression technique differential pulse coded modulation (DPCM) which is lossless followed by simple, efficient and variable length encoding called Golomb-Rice coding. The proposed ideal image compressor is compatible with any image sensors which are low power and commercially available that outputs the pixels of the images in a raster scan manner. It will evict the need of the memory buffer and temporary storage needed in the transform based coding methods. Additional compression acquired by the introduction of the corner clipping algorithm to remove the distorted corners present in the endoscopic images. Compared to other algorithms aims at wireless capsule endoscopy, the proposed ideal algorithm is highly efficient with a high compression ratio as well as very high reconstruction peak signal-to-noise ratio.