Compression of cDNA and inkjet microarray images

Microarray image compression: SLOCO and the effect of information loss

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compressed using lossless method and non-QRS regions are compressed using lossy method, which improves the compression ratio and minimise the reconstruction error between original and reconstructed ECG signal. All tested ECG records are selected from the PTB database. Statistical results show that the compression performance of the proposed method is better than both lossless and lossy ECG data compression technique. The average compression ratio (CR) and percent root mean square difference (PRD) obtained using proposed ECG data compression method are 27.40 and 4.753 respectively, which is better than other existing ECG data compression method.

Current gene-expression microarrays carry enormous amounts of information. Compression is necessary for efficient distribution and storage. This paper examines JPEG2000 compression of cDNA microarray images and addresses the accuracy of classification and feature selection based on decompressed images. Among other options, we choose JPEG2000 because it is the latest international standard for image compression and offers lossy-to-lossless compression while achieving high lossless compression ratios on microarray images. The performance of JPEG2000 has been tested on three real data sets at different compression ratios, ranging from lossless to 45:1. The effects of JPEG2000 compression/decompression on differential expression detection and phenotype classification have been examined. There is less than a 4% change in differential detection at compression rates as high as 20:1, with detection accuracy suffering less than 2% for moderate to high intensity genes, and there is no significant effect on classification at rates as high as 35:1. The supplementary material is available at http://gsp.tamu.edu/web2/Compression.

cDNA microarrays is one of the most fundamental and powerful tools in biotechnology. Despite its relatively late discovery in 1995, it has since been utilized in many biomedical applications such as cancer research, infectious disease diagnosis and treatment, toxicology research, pharmacology research, and agricultural development. The reason for its broad use is that it enables scientists to analyze simultaneously the expression levels of thousands of genes over different samples (Leung et al., 2003). More precisely, the process of a microarray experiment (Campbell et al., 2007) starts with the selection of a set of DNA probes that are of particular interest. A robot places the selected DNA probes on a glass slide, creating an invisible array of DNA dots. Two distinct populations of mRNAs (messenger RNAs) are then isolated from a control sample (i.e a cell developed under normal conditions) and a test sample (i.e. a cell developed under a specific treatment). The mRNA populations are reversely transcribed into cDNA (complementary DNA) populations which in turn are colored with separate fluorescent dyes of different wavelengths (i.e. Cy3 and Cy5). The dyed cDNA populations are mixed with purified water and the solution is placed on the glass slide in order for the cDNA populations to be hybridized with the slide’s DNA dots. Finally, the hybridized glass slide is fluorescently scanned twice; one scan for each dye’s wavelength. Hence, two digital images are produced, one for each population of mRNA. Each digital image contains a number of spots (corresponding to the DNA-cDNA dots) of various fluorescence intensities. Given that the intensity of each spot is proportional to the hybridization level of the cDNAs and the DNA dots, the gene expression information is obtained by analyzing the digital images. As stated by Yang et al (Yang et. al, 2002), the process of analyzing a microarray image can be divided into three main phases, namely: “Gridding”, “Spot-Segmentation” and “SpotIntensity extraction”. During the 1st phase, the microarray image is segmented into numerous compartments, each containing one individual spot and background. During the 2nd phase each compartment is individually segmented into a spot area and a background area, while during the 3rd phase the brightness of each spot is calculated. The expressionlevels of the genes in these spots are a direct result of their individual brightness. Amongst the stages of the microarray-image analysis, spot-segmentation remains the most challenging one. Ideally, the existing spots inside the microarray image are aligned in 2D array layouts. These ‘ideal spots’ also have a circular 2D shape with fixed diameters, while

Wavelets are defined as mathematical functions that segment the data into different frequency levels. We can easily capture the fine and coarse details of an image or signal referred to as a subband. And it also helps in subband thresholding to achieve good compression performance. In recent days in telemedicine services, the handling of medical images is prominently increasing and it leads to the demand for medical image compression. While compressing the medical images, we have to concentrate on the data that holds important information, and at the same time, it must retain the image quality. Near-Lossless compression plays an essential role to achieve a better compression ratio than lossy compression and provides better quality than lossless compression. In this paper, we analyzed the sub-banding of Discrete Wavelet Transform (DWT) using different types of wavelets and made an optimal selection of wavelets for subband thresholding to attain a good compression performance with an application to medical images. We used Set Partitioning In Hierarchical Trees (SPIHT) compression scheme to test the compression performance of different wavelets. The Peak Signal to Noise Ratio (PSNR), Bits Per Pixel (BPP), Compression Ratio, and percentage of number of zeros are used as metrics to assess the performance of all the selected wavelets. And to find out its efficiency in possessing the essential information of medical images, the subband of the selected wavelets is further utilized to devise the near-lossless compression scheme for medical images.

<p>Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. Clustering algorithms have been used for microarray image segmentation with an advantage that they are not restricted to a particular shape and size for the spots. Instead of using single feature clustering algorithm, this paper presents multiple feature clustering algorithm with three features for each pixel such as pixel intensity, distance from the center of the spot and median of surrounding pixels. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user. In this paper, a new algorithm based on empirical mode decomposition algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering. It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that multiple feature Fuzzy C-means has segmented the microarray image more accurately than other algorithms.</p>

<p>The projection into 2030 of the climate data volume increase brings an important challenge to the climate science community. This is particularly true for the CMIP7 that is projected to need about an Exabyte of storage capacity. Error-bounded lossy compression is explored as a potential solution to the above problem by different climate research teams. Several lossy compression schemes have been proposed leveraging different forms of decorrelation (transforms, prediction, HoSVD, DNN), quantization (linear, non-linear, vector), and encoding (dictionary-based, variable length, etc.) algorithms. Our experience with different applications shows that the compression methods often need to be customized and optimized to fit the specificities of the datasets to compress and the user requirements on the compression quality, ratio, and throughput. However, none of the existing lossy compression software for scientific data has been designed to be customizable. To address this issue, we developed SZ3, an innovative customizable, modular compression framework. SZ3 is a full C++ refactoring of SZ2 enabling the specialization, addition, or removal of each stage of the lossy compression pipeline to fit the specific characteristics of the datasets to compress and the use-case requirements. This extreme flexibility allows adapting SZ3 to many different use-cases, from ultra-high compression for visualization to ultra-high-speed compression between the CPU (or GPU) and the memory. Thanks to its unique set of features: customization, high compression ratio, high compression throughput, and excellent accuracy preservation, SZ3 won a 2021 R&D100 award. In this presentation, we present SZ3 and a new data prediction-based decorrelation method that significantly improves the compression ratios for climate datasets over the state-of-the-art lossy compressors, while preserving the same data accuracy. Experiments based on CESM datasets show that SZ3 can lead to up to 300% higher compression ratios than SZ2 with the same compression error bound and similar compression throughput.</p>

To review the literature on lossy compression in dental radiography and to discuss the importance and suitability of the methodology used for evaluation of image compression. A search of Medline (from 1966 to October 2004) was undertaken with the search expression "(Radiography, dental) and compression". Inclusion criterion was that the reference should be evaluating the effect of lossy image compression on diagnostic accuracy. For all included studies, information in relation to mode of image acquisition, image content, image compression, image display, and method of image evaluation was extracted. 12 out of 32 papers were included in the review. The design of these 12 studies was found to vary considerably. Parameters used to express the degree of information loss (DIL) were either or both compression ratio (CR) and compression level (CL). The highest acceptable CR reported in the studies ranged from 3.6% to 15.4%. Furthermore, different CR values were proposed even for the same diagnostic task, for example, for caries diagnosis CR ranged from 6.2% to 11.1%. Lossy image compression can be used in clinical radiology if it does not conflict with national law. However, the acceptable DIL is difficult to express and standardize. CR is probably not suitable to express DIL, because it is image content dependent. CL is also probably not suitable to express DIL because of the lack of compression software standardization.

A contrast-detail (CD) experiment was performed to study the effect of lossy compression on computed radiographic (CR) images. Digital CR images of a phantom were compressed by quantizing the full-frame discrete cosine transform and Huffman encoding the result. Since low-contrast detectability is directly linked to an important radiological task, namely, the detection of noncalcified pulmonary nodules in adult chest radiographs, the goal of the study was to quantify any loss in low-contrast detectability due to compression. Compression ratios varied significantly among compressed images, despite the use of fixed compression parameters; detectability could be specified by a single parameter of a CD curve; there was no significant reduction in detectability for an average compression ratio of 11:1; and, there was a statistically significant degradation in detectability for an average compression ratio of 125:1.

Analysing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep Learning technique based on vector quantisation to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows. The deep learning framework is composed of convolutional layers and incorporates physical constraints on the flow, such as preserving incompressibility and global statistical characteristics of the velocity gradients. The accuracy of the model is assessed using statistical, comparison-based similarity and physics-based metrics. The training data set is produced from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow. The performance of this lossy data compression scheme is evaluated not only with unseen data from the stationary, isotropic turbulent flow, but also with data from decaying isotropic turbulence, a Taylor–Green vortex flow, and a turbulent channel flow. Defining the compression ratio (CR) as the ratio of original data size to the compressed one, the results show that our model based on vector quantisation can offer CR with a mean square error (MSE) of , and predictions that faithfully reproduce the statistics of the flow, except at the very smallest scales where there is some loss. Compared to the recent study of Glaws. et al. [Deep learning for in situ data compression of large turbulent flow simulations. Phys Rev Fluids. 2020;5(11):114602], which was based on a conventional autoencoder (where compression is performed in a continuous space), our model improves the CR by more than 30%, and reduces the MSE by an order of magnitude. Our compression model is an attractive solution for situations where fast, high quality and low-overhead encoding and decoding of large data are required.

Entropy encoder for low-power low-resources high-quality CFA image compression

For instruction decompression, techniques such as single buffering, double buffering and pipelining have been proposed. However, due to jumping penalty, these techniques incur more delays in pipeline or system has to be stopped to refill the cache buffers. A Pipeline with Back-up for Flushing (PBF) technique has been developed that incurs no delay and without stopping due to jumping. However, the first instruction of each basic block should not be compressed and be put in another ROM, and thus the compression ratio should be sacrificed. This paper improves the PBF technique such that a single program ROM needed only. The simulation results for several benchmarks show that the average compression ratio is decreased about 11%, and the hardware cost deceased about 8%.

To improve the performance of the cone bit bearing seal, experimental study and simulation analysis of seal failure were carried out, and optimization of the seal was conducted. Results show that damages are located on the shoulder of the rubber rings due to high shear stress caused by large compression ratio, and serious wear appears on the sealing face of the metal rings because of unreasonable contact pressure distribution. Optimization was conducted by finite element method on three aspects: the dip angles (3°–8°) of the sealing face, the widths (1.3–3.9 mm) of the sealing face, and the back support structure (10°–15°). These three aspects were optimized one by one. Each aspect was optimized by different values, with the average compression ratio and the contact pressure distribution of the sealing face acting as evaluations. The optimal value of the previous optimization step performs as the static value in the next step, and finally, the optimized structure was selected from the last step. The performance of the optimized seal is significantly improved. The compression ratio of the optimized seal rubber drops to less than 14%. The wear mount of the new structure reduces to 20%–38% of the original structure.

A lossy compression scheme for Bayer images is proposed in this paper. Recently, it was found that compression-first schemes outperform the conventional demosaicking-first schemes in terms of output image quality. Balanced multiwavelet packet transforms effectively remove CFA image correlation between frequency bands. Wavelet coefficients shuffling exploring subband correlation makes it suitable for zerotree coding. Improved SPIHT algorithm further exploits data correlation in different direction under the same resolution using one symbol to denote three zerotrees while the SPIHT algorithm using three symbols. The simulation results show that: proposed compression scheme at low bit rate compared with JPEG2000 has better performance, and low complexity. CPSNR is 2db higher than JPEG2000 at bit rate 1 and is 0.7db higher than JPEG2000 at bit rate 2.

In this paper, we evaluate lossless and lossy compression techniques to compress quantitative phase images of red blood cells (RBCs) obtained by an off-axis digital holographic microscopy (DHM). The RBC phase images are numerically reconstructed from their digital holograms and are stored in 16-bit unsigned integer format. In the case of lossless compression, predictive coding of JPEG lossless (JPEG-LS), JPEG2000, and JP3D are evaluated, and compression ratio (CR) and complexity (compression time) are compared against each other. It turns out that JP2k can outperform other methods by having the best CR. In the lossy case, JP2k and JP3D with different CRs are examined. Because some data is lost in a lossy way, the degradation level is measured by comparing different morphological and biochemical parameters of RBC before and after compression. Morphological parameters are volume, surface area, RBC diameter, sphericity index, and the biochemical cell parameter is mean corpuscular hemoglobin (MCH). Experimental results show that JP2k outperforms JP3D not only in terms of mean square error (MSE) when CR increases, but also in compression time in the lossy compression way. In addition, our compression results with both algorithms demonstrate that with high CR values the three-dimensional profile of RBC can be preserved and morphological and biochemical parameters can still be within the range of reported values.