An image multiresolution representation for lossless and lossy compression

Sharing massive biomedical data at magnitudes lower bandwidth using implicit neural function

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning-based methods demand huge training data and are difficult to generalize. Here, we propose to conduct Biomedical data compRession with Implicit nEural Function (BRIEF) by representing the target data with compact neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves 2[Formula: see text]3 orders of magnitude compression on diverse biomedical data at significantly higher fidelity than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, and supports customized spatially varying fidelity. BRIEF's multifold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate low-bandwidth data sharing and promote collaboration and progress in the biomedical field.

A Novel 3D Reversible Data Hiding Scheme Based on Integer-Reversible Krawtchouk Transform for IoMT

To avoid rounding errors associated with the limited representation of significant digits when applying the floating-point Krawtchouk transform in image processing, we present an integer and reversible version of the Krawtchouk transform (IRKT). This proposed IRKT generates integer-valued coefficients within the Krawtchouk domain, seamlessly aligning with the integer representation commonly utilized in lossless image applications. Building upon the IRKT, we introduce a novel 3D reversible data hiding (RDH) algorithm designed for the secure storage and transmission of extensive medical data within the IoMT (Internet of Medical Things) sector. Through the utilization of the IRKT-based 3D RDH method, a substantial amount of additional data can be embedded into 3D carrier medical images without augmenting their original size or compromising information integrity upon data extraction. Extensive experimental evaluations substantiate the effectiveness of the proposed algorithm, particularly regarding its high embedding capacity, imperceptibility, and resilience against statistical attacks. The integration of this proposed algorithm into the IoMT sector furnishes enhanced security measures for the safeguarded storage and transmission of massive medical data, thereby addressing the limitations of conventional 2D RDH algorithms for medical images.

Use of Full-quality DICOM Images Compared to Minimally Compressed Mammograms in JPEG Format for Radiology Training: A Study From Radiologist and Radiographer Perspectives

BACKGROUND

Running online training in mammography interpretation poses a challenge to radiologists and reporting radiographers due to the large size of digital mammograms in DICOM format and limited bandwidth capabilities of the users for image transmission. This study aims to compare image quality between the full-quality with minimal compressed JPEG and DICOM format of mammograms on a diagnostic monitor through the evaluation of radiologists and radiographers.

METHODS

Twelve participants including six radiologists and six radiographers participated as observers in this study. The observers viewed 60 2D digital mammography screening cases (22 cancer and 38 normal cases) in DICOM and minimal compressed JPEG formats on a 5MP diagnostic monitor. A 5-point Likert scale was provided for observers to compare the quality of mammograms between the two formats, with text anchors indicating to one image being significantly better, slightly better or of equal quality in terms of technical and diagnostic aspects. Nonparametric descriptive statistics were used to evaluate the ratings of radiologists and radiographers in different characteristics of mammograms of two image formats.

RESULTS

The DICOM and JPEG images were statistically equivalent through ratings from radiographers in brightness, contrast, dynamic range, sharpness, no significant distortion, no significant noise, and background homogeneity in all mammograms. Similarly, most radiologists rated DICOM and JPEG images clinically and statistically equivalent with respect to difficulty of interpretation, brightness, contrast, dynamic range, sharpness, the appearance of Cooper's ligaments, visibility of subtle microcalcifications, visibility of structures at the margins of the breast. Normal cases were marginally favored by radiologists in DICOM format (ranging from 0.4% to 5.3%) while cancer cases in JPEG (ranging from 0.8% to 7.6%) received slightly higher rating.

CONCLUSIONS

Findings showed that baseline full-quality with minimal compression JPEG was equivalent to the DICOM format of full-field digital mammograms which suggests that this type of JPEG could be used for online training and education in radiology.

Preparing CT imaging datasets for deep learning in lung nodule analysis: Insights from four well-known datasets

BACKGROUND

Deep learning is an important means to realize the automatic detection, segmentation, and classification of pulmonary nodules in computed tomography (CT) images. An entire CT scan cannot directly be used by deep learning models due to image size, image format, image dimensionality, and other factors. Between the acquisition of the CT scan and feeding the data into the deep learning model, there are several steps including data use permission, data access and download, data annotation, and data preprocessing. This paper aims to recommend a complete and detailed guide for researchers who want to engage in interdisciplinary lung nodule research of CT images and Artificial Intelligence (AI) engineering.

METHODS

The data preparation pipeline used the following four popular large-scale datasets: LIDC-IDRI (Lung Image Database Consortium image collection), LUNA16 (Lung Nodule Analysis 2016), NLST (National Lung Screening Trial) and NELSON (The Dutch-Belgian Randomized Lung Cancer Screening Trial). The dataset preparation is presented in chronological order.

FINDINGS

The different data preparation steps before deep learning were identified. These include both more generic steps and steps dedicated to lung nodule research. For each of these steps, the required process, necessity, and example code or tools for actual implementation are provided.

DISCUSSION AND CONCLUSION

Depending on the specific research question, researchers should be aware of the various preparation steps required and carefully select datasets, data annotation methods, and image preprocessing methods. Moreover, it is vital to acknowledge that each auxiliary tool or code has its specific scope of use and limitations. This paper proposes a standardized data preparation process while clearly demonstrating the principles and sequence of different steps. A data preparation pipeline can be quickly realized by following these proposed steps and implementing the suggested example codes and tools.

High-Throughput Lossy-to-Lossless 3D Image Compression

The rapid increase in medical and biomedical image acquisition rates has opened up new avenues for image analysis, but has also introduced formidable challenges. This is evident, for example, in selective plane illumination microscopy where acquisition rates of about 1-4 GB/s sustained over several days have redefined the scale of I/O bandwidth required by image analysis tools. Although the effective bandwidth could, principally, be increased by lossy-to-lossless data compression, this is of limited value in practice due to the high computational demand of current schemes such as JPEG2000 that reach compression throughput of one order of magnitude below that of image acquisition. Here we present a novel lossy-to-lossless data compression scheme with a compression throughput well above 4 GB/s and compression rates and rate-distortion curves competitive with those achieved by JPEG2000 and JP3D.

Multi-Resolution Compressive Spectral Imaging Reconstruction from Single Pixel Measurements

Massive amounts of data in spectral imagery increase acquisition, storing and processing costs. Compressive spectral imaging (CSI) methods allow the reconstruction of spatial and spectral information from a small set of random projections. The single pixel camera is a low cost optical architecture which enables the compressive acquisition of spectral images. Traditional CSI reconstruction methods obtain a sparse approximation of the underlying spatial and spectral information, however the complexity of these algorithms increases in proportion to the dimensionality of the data. This work proposes a multiresolution (MR) CSI reconstruction approach from single pixel camera measurements that exploits spectral similarities between pixels to group them in super-pixels such that the total number of unknowns in the inverse problem is reduced. Specifically, two different types of super-pixels are considered: rectangular and irregular structures. Simulation and experimental results show that the proposed MR scheme improves reconstruction quality in up to 6dB of PSNR and reconstruction time in up to 90% with respect to the traditional full resolution reconstructions.

Onboard Image Processing System for Hyperspectral Sensor

Onboard image processing systems for a hyperspectral sensor have been developed in order to maximize image data transmission efficiency for large volume and high speed data downlink capacity. Since more than 100 channels are required for hyperspectral sensors on Earth observation satellites, fast and small-footprint lossless image compression capability is essential for reducing the size and weight of a sensor system. A fast lossless image compression algorithm has been developed, and is implemented in the onboard correction circuitry of sensitivity and linearity of Complementary Metal Oxide Semiconductor (CMOS) sensors in order to maximize the compression ratio. The employed image compression method is based on Fast, Efficient, Lossless Image compression System (FELICS), which is a hierarchical predictive coding method with resolution scaling. To improve FELICS’s performance of image decorrelation and entropy coding, we apply a two-dimensional interpolation prediction and adaptive Golomb-Rice coding. It supports progressive decompression using resolution scaling while still maintaining superior performance measured as speed and complexity. Coding efficiency and compression speed enlarge the effective capacity of signal transmission channels, which lead to reducing onboard hardware by multiplexing sensor signals into a reduced number of compression circuits. The circuitry is embedded into the data formatter of the sensor system without adding size, weight, power consumption, and fabrication cost.

[Symbol: see text]2 Optimized predictive image coding with [Symbol: see text]∞ bound

In many scientific, medical, and defense applications of image/video compression, an [Symbol: see text]∞ error bound is required. However, pure[Symbol: see text]∞-optimized image coding, colloquially known as near-lossless image coding, is prone to structured errors such as contours and speckles if the bit rate is not sufficiently high; moreover, most of the previous [Symbol: see text]∞-based image coding methods suffer from poor rate control. In contrast, the [Symbol: see text]2 error metric aims for average fidelity and hence preserves the subtlety of smooth waveforms better than the ∞ error metric and it offers fine granularity in rate control, but pure [Symbol: see text]2-based image coding methods (e.g., JPEG 2000) cannot bound individual errors as the [Symbol: see text]∞-based methods can. This paper presents a new compression approach to retain the benefits and circumvent the pitfalls of the two error metrics. A common approach of near-lossless image coding is to embed into a DPCM prediction loop a uniform scalar quantizer of residual errors. The said uniform scalar quantizer is replaced, in the proposed new approach, by a set of context-based [Symbol: see text]2-optimized quantizers. The optimization criterion is to minimize a weighted sum of the [Symbol: see text]2 distortion and the entropy while maintaining a strict [Symbol: see text]∞ error bound. The resulting method obtains good rate-distortion performance in both [Symbol: see text]2 and [Symbol: see text]∞ metrics and also increases the rate granularity. Compared with JPEG 2000, the new method not only guarantees lower [Symbol: see text]∞ error for all bit rates, but also it achieves higher PSNR for relatively high bit rates.

WAVELET-BASED LOSSY-TO-LOSSLESS MEDICAL IMAGE COMPRESSION USING DYNAMIC VQ AND SPIHT CODING

As the coming era of digitized medical information, a close-at-hand challenge to deal with is the storage and transmission requirement of enormous data, including medical images. Compression is one of the indispensable techniques to solve this problem. In this work, we propose a dynamic vector quantization (DVQ) scheme with distortion-constrained codebook replenishment (DCCR) mechanism in wavelet domain. In the DVQ-DCCR mechanism, a novel tree-structure vector and the well-known SPIHT technique are combined to provide excellent coding performance in terms of compression ratio and peak signal-to-noise ratio for lossy compression. For the lossless compression in similar scheme, we replace traditional 9/7 wavelet filters by 5/3 filters and implement the wavelet transform in the lifting structure. Furthermore, a detection strategy is proposed to stop the SPIHT coding for less significant bit planes, where SPIHT begins to lose its coding efficiency. Experimental results show that the proposed algorithm is superior to SPIHT with the arithmetic coding in both lossy and lossless compression for all tested images.

Scalable L-infinite coding of meshes

The paper investigates the novel concept of local-error control in mesh geometry encoding. In contrast to traditional mesh-coding systems that use the mean-square error as target distortion metric, this paper proposes a new L-infinite mesh-coding approach, for which the target distortion metric is the L-infinite distortion. In this context, a novel wavelet-based L-infinite-constrained coding approach for meshes is proposed, which ensures that the maximum error between the vertex positions in the original and decoded meshes is lower than a given upper bound. Furthermore, the proposed system achieves scalability in L-infinite sense, that is, any decoding of the input stream will correspond to a perfectly predictable L-infinite distortion upper bound. An instantiation of the proposed L-infinite-coding approach is demonstrated for MESHGRID, which is a scalable 3D object encoding system, part of MPEG-4 AFX. In this context, the advantages of scalable L-infinite coding over L-2-oriented coding are experimentally demonstrated. One concludes that the proposed L-infinite mesh-coding approach guarantees an upper bound on the local error in the decoded mesh, it enables a fast real-time implementation of the rate allocation, and it preserves all the scalability features and animation capabilities of the employed scalable mesh codec.

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

Image interpolation for progressive transmission by using radial basis function networks

This paper investigates the application of a radial basis function network (RBFN) to a hierarchical image coding for progressive transmission. The RBFN is then used to generate an interpolated image from the subsampled version. An efficient method of computing the network parameters is developed for reduction in computational and memory requirements. The coding method does not suffer from problems of blocking effect and can produce the coarsest image quickly. Quantization error effects introduced at one stage are considered in decoding images at the following stages, thus allowing lossless progressive transmission.

Entropy improvement by the Temporal-Window method for alternating and non-alternating 3D wavelet transform over angiographies

The three-dimensional wavelet transform (3D-WT) has been proposed for volumetric data coding, since it can provide lossless coding and top-quality reconstruction: two key features highly relevant to medical imaging applications. In this paper, we present experimental results for four new algorithms based on the Classic 3D-WT. The proposed algorithms are capable of obtaining the wavelet coefficients after the spatial and, mainly, the temporal decomposition processes, reducing most redundancies in the video sequence and getting lower entropy values than the Classic algorithm. The new algorithms are based on the Temporal-Window method for carrying out the temporal decomposition. We have conducted a set of experimental evaluations for a representative data set of a modality of intrinsically volumetric medical imaging: angiography sequences.

A wavelet-based two-stage near-lossless coder

In this paper, we present a two-stage near-lossless compression scheme. It belongs to the class of "lossy plus residual coding" and consists of a wavelet-based lossy layer followed by arithmetic coding of the quantized residual to guarantee a given L(infinity) error bound in the pixel domain. We focus on the selection of the optimum bit rate for the lossy layer to achieve the minimum total bit rate. Unlike other similar lossy plus lossless approaches using a wavelet-based lossy layer, the proposed method does not require iteration of decoding and inverse discrete wavelet transform in succession to locate the optimum bit rate. We propose a simple method to estimate the optimal bit rate, with a theoretical justification based on the critical rate argument from the rate-distortion theory and the independence of the residual error.

Adaptive prediction trees for image compression

This paper presents a complete general-purpose method for still-image compression called adaptive prediction trees. Efficient lossy and lossless compression of photographs, graphics, textual, and mixed images is achieved by ordering the data in a multicomponent binary pyramid, applying an empirically optimized nonlinear predictor, exploiting structural redundancies between color components, then coding with hex-trees and adaptive runlength/Huffman coders. Color palettization and order statistics prefiltering are applied adaptively as appropriate. Over a diverse image test set, the method outperforms standard lossless and lossy alternatives. The competing lossy alternatives use block transforms and wavelets in well-studied configurations. A major result of this paper is that predictive coding is a viable and sometimes preferable alternative to these methods.

Lossless compression of color mosaic images

Lossless compression of color mosaic images poses a unique and interesting problem of spectral decorrelation of spatially interleaved R, G, B samples. We investigate reversible lossless spectral-spatial transforms that can remove statistical redundancies in both spectral and spatial domains and discover that a particular wavelet decomposition scheme, called Mallat wavelet packet transform, is ideally suited to the task of decorrelating color mosaic data. We also propose a low-complexity adaptive context-based Golomb-Rice coding technique to compress the coefficients of Mallat wavelet packet transform. The lossless compression performance of the proposed method on color mosaic images is apparently the best so far among the existing lossless image codecs.

Regularization operators for natural images based on nonlinear perception models

Image restoration requires some a priori knowledge of the solution. Some of the conventional regularization techniques are based on the estimation of the power spectrum density. Simple statistical models for spectral estimation just take into account second-order relations between the pixels of the image. However, natural images exhibit additional features, such as particular relationships between local Fourier or wavelet transform coefficients. Biological visual systems have evolved to capture these relations. We propose the use of this biological behavior to build regularization operators as an alternative to simple statistical models. The results suggest that if the penalty operator takes these additional features in natural images into account, it will be more robust and the choice of the regularization parameter is less critical.

Nonlinear image representation for efficient perceptual coding

Image compression systems commonly operate by transforming the input signal into a new representation whose elements are independently quantized. The success of such a system depends on two properties of the representation. First, the coding rate is minimized only if the elements of the representation are statistically independent. Second, the perceived coding distortion is minimized only if the errors in a reconstructed image arising from quantization of the different elements of the representation are perceptually independent. We argue that linear transforms cannot achieve either of these goals and propose, instead, an adaptive nonlinear image representation in which each coefficient of a linear transform is divided by a weighted sum of coefficient amplitudes in a generalized neighborhood. We then show that the divisive operation greatly reduces both the statistical and the perceptual redundancy amongst representation elements. We develop an efficient method of inverting this transformation, and we demonstrate through simulations that the dual reduction in dependency can greatly improve the visual quality of compressed images.

Optimization of integer wavelet transforms based on difference correlation structures

In this paper, a novel lifting integer wavelet transform based on difference correlation structure (DCCS-LIWT) is proposed. First, we establish a relationship between the performance of a linear predictor and the difference correlations of an image. The obtained results provide a theoretical foundation for the following construction of the optimal lifting filters. Then, the optimal prediction lifting coefficients in the sense of least-square prediction error are derived. DCCS-LIWT puts heavy emphasis on image inherent dependence. A distinct feature of this method is the use of the variance-normalized autocorrelation function of the difference image to construct a linear predictor and adapt the predictor to varying image sources. The proposed scheme also allows respective calculations of the lifting filters for the horizontal and vertical orientations. Experimental evaluation shows that the proposed method produces better results than the other well-known integer transforms for the lossless image compression.

Wavelet-Based Compression of M-FISH Images

Multiplex fluorescence in situ hybridization (M-FISH) is a recently developed technology that enables multi-color chromosome karyotyping for molecular cytogenetic analysis. Each M-FISH image set consists of a number of aligned images of the same chromosome specimen captured at different optical wavelength. This paper presents embedded M-FISH image coding (EMIC), where the foreground objects/chromosomes and the background objects/images are coded separately. We first apply critically sampled integer wavelet transforms to both the foreground and the background. We then use object-based bit-plane coding to compress each object and generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of the foreground and the background. For efficient arithmetic coding of bit planes, we propose a method of designing an optimal context model that specifically exploits the statistical characteristics of M-FISH images in the wavelet domain. Our experiments show that EMIC achieves nearly twice as much compression as Lempel-Ziv-Welch coding. EMIC also performs much better than JPEG-LS and JPEG-2000 for lossless coding. The lossy performance of EMIC is significantly better than that of coding each M-FISH image with JPEG-2000.

Wavelet-based lossy-to-lossless ECG compression in a unified vector quantization framework

In a prior work, a wavelet-based vector quantization (VQ) approach was proposed to perform lossy compression of electrocardiogram (ECG) signals. In this paper, we investigate and fix its coding inefficiency problem in lossless compression and extend it to allow both lossy and lossless compression in a unified coding framework. The well-known 9/7 filters and 5/3 integer filters are used to implement the wavelet transform (WT) for lossy and lossless compression, respectively. The codebook updating mechanism, originally designed for lossy compression, is modified to allow lossless compression as well. In addition, a new and cost-effective coding strategy is proposed to enhance the coding efficiency of set partitioning in hierarchical tree (SPIHT) at the less significant bit representation of a WT coefficient. ECG records from the MIT/BIH Arrhythmia and European ST-T Databases are selected as test data. In terms of the coding efficiency for lossless compression, experimental results show that the proposed codec improves the direct SPIHT approach and the prior work by about 33% and 26%, respectively.

Performance improvement of the SPIHT coder based on statistics of medical ultrasound images in the wavelet domain

This paper proposes some modifications to the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) image coder based on statistical analysis of the wavelet coefficients across various subbands and scales, in a medical ultrasound (US) image. The original SPIHT algorithm codes all the subbands with same precision irrespective of their significance, whereas the modified algorithm processes significant subbands with more precision and ignores the least significant subbands. The statistical analysis shows that most of the image energy in ultrasound images lies in the coefficients of vertical detail subbands while diagonal subbands contribute negligibly towards total image energy. Based on these statistical observations, this work presents a new modified SPIHT algorithm, which codes the vertical subbands with more precision while neglecting the diagonal subbands. This modification speeds up the coding/decoding process as well as improving the quality of the reconstructed medical image at low bit rates. The experimental results show that the proposed method outperforms the original SPIHT on average by 1.4 dB at the matching bit rates when tested on a series of medical ultrasound images. Further, the proposed algorithm needs 33% less memory as compared to the original SPIHT algorithm.

Space-frequency quantiser design for ultrasound image compression based on minimum description length criterion

The paper addresses the problem of how the spatial quantisation mode and subband adaptive uniform scalar quantiser can be jointly optimised in the minimum description length (MDL) framework for compression of ultrasound images. It has been shown that the statistics of wavelet coefficients in the medical ultrasound (US) image can be better approximated by the generalised Student t-distribution. By combining these statistics with the operational rate-distortion (RD) criterion, a space-frequency quantiser (SFQ) called the MDL-SFQ was designed, which used an efficient zero-tree quantisation technique for zeroing out the tree-structured sets of wavelet coefficients and an adaptive scalar quantiser to quantise the non-zero coefficients. The algorithm used the statistical 'variance of quantisation error' to achieve the different bit-rates ranging from near-lossless to lossy compression. Experimental results showed that the proposed coder outperformed the set partitioning in hierarchical trees (SPIHT) image coder both quantitatively and qualitatively. It yielded an improved compression performance of 1.01 dB over the best zero-tree based coder SPIHIT at 0.25 bits per pixel when averaged over five ultrasound images.

Morse description and geometric encoding of digital elevation maps

Two complementary geometric structures for the topographic representation of an image are developed in this work. The first one computes a description of the Morse-topological structure of the image, while the second one computes a simplified version of its drainage structure. The topographic significance of the Morse and drainage structures of digital elevation maps (DEMs) suggests that they can been used as the basis of an efficient encoding scheme. As an application, we combine this geometric representation with an interpolation algorithm and lossless data compression schemes to develop a compression scheme for DEMs. This algorithm achieves high compression while controlling the maximum error in the decoded elevation map, a property that is necessary for the majority of applications dealing with DEMs. We present the underlying theory and compression results for standard DEM data.

Lossless compression of volumetric medical images with improved three-dimensional SPIHT algorithm

This article presents a lossless compression of volumetric medical images with the improved three-dimensional (3-D) set partitioning in hierarchical tree (SPIHT) algorithm that searches on asymmetric trees. The tree structure links wavelet coefficients produced by 3-D reversible integer wavelet transforms. Experiments show that the lossless compression with the improved 3-D SPIHT gives improvement about 42% on average over two-dimensional techniques and is superior to those of prior results of 3-D techniques. In addition, we can easily apply different numbers of decomposition between the transaxial and axial dimensions, which is a desirable function when the coding unit of a group of slices is limited in size.

Region-based wavelet coding methods for digital mammography

Spatial resolution and contrast sensitivity requirements for some types of medical image techniques, including mammography, delay the implementation of new digital technologies, namely, computer-aided diagnosis, picture archiving and communications systems, or teleradiology. In order to reduce transmission time and storage cost, an efficient data-compression scheme to reduce digital data without significant degradation of medical image quality is needed. In this study, we have applied two region-based compression methods to digital mammograms. In both methods, after segmenting the breast region, a region-based discrete wavelet transform is applied, followed by an object-based extension of the set partitioning in hierarchical trees (OB-SPIHT) coding algorithm in one method, and an object-based extension of the set partitioned embedded block (OB-SPECK) coding algorithm in the other. We have compared these specific implementations against the original SPIHT and the new standard JPEG 2000, both using reversible and irreversible filters, on five digital mammograms compressed at rates ranging from 0.1 to 1.0 bit per pixel (bbp). Distortion was evaluated for all images and compression rates by the peak signal-to-noise ratio. For all images, OB-SPIHT and OB-SPECK performed substantially better than the traditional SPIHT and JPEG 2000, and a slight difference in performance was found between them. A comparison applying SPIHT and the standard JPEG 2000 to the same set of images with the background pixels fixed to zero was also carried out, obtaining similar implementation as region-based methods. For digital mammography, region-based compression methods represent an improvement in compression efficiency from full-image methods, also providing the possibility of encoding multiple regions of interest independently.

Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms

We study lossy-to-lossless compression of medical volumetric data using three-dimensional (3-D) integer wavelet transforms. To achieve good lossy coding performance, it is important to have transforms that are unitary. In addition to the lifting approach, we first introduce a general 3-D integer wavelet packet transform structure that allows implicit bit shifting of wavelet coefficients to approximate a 3-D unitary transformation. We then focus on context modeling for efficient arithmetic coding of wavelet coefficients. Two state-of-the-art 3-D wavelet video coding techniques, namely, 3-D set partitioning in hierarchical trees (Kim et al., 2000) and 3-D embedded subband coding with optimal truncation (Xu et al., 2001), are modified and applied to compression of medical volumetric data, achieving the best performance published so far in the literature-both in terms of lossy and lossless compression.

Wavelet coding of volumetric medical datasets

Several techniques based on the three-dimensional (3-D) discrete cosine transform (DCT) have been proposed for volumetric data coding. These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications. This paper gives an overview of several state-of-the-art 3-D wavelet coders that do meet these requirements and proposes new compression methods exploiting the quadtree and block-based coding concepts, layered zero-coding principles, and context-based arithmetic coding. Additionally, a new 3-D DCT-based coding scheme is designed and used for benchmarking. The proposed wavelet-based coding algorithms produce embedded data streams that can be decoded up to the lossless level and support the desired set of functionality constraints. Moreover, objective and subjective quality evaluation on various medical volumetric datasets shows that the proposed algorithms provide competitive lossy and lossless compression results when compared with the state-of-the-art.

On exploiting geometric constraint of image wavelet coefficients

In this paper, we investigate the problem of how to exploit geometric constraint of edges in wavelet-based image coding.The value of studying this problem is the potential coding gain brought by improved probabilistic models of wavelet high-band coefficients. Novel phase shifting and prediction algorithms are derived in the wavelet space. It is demonstrated that after resolving the phase uncertainty, high-band wavelet coefficients can be better modeled by biased-mean probability models rather than the existing zero-mean ones. In lossy coding, the coding gain brought by the biased-mean model is quantitatively analyzed within the conventional DPCM coding framework. Experiment results have shown the proposed phase shifting and prediction scheme improves both subjective and objective performance of wavelet-based image coders.

Lossless image compression with projection-based and adaptive reversible integer wavelet transforms

Reversible integer wavelet transforms are increasingly popular in lossless image compression, as evidenced by their use in the recently developed JPEG2000 image coding standard. In this paper, a projection-based technique is presented for decreasing the first-order entropy of transform coefficients and improving the lossless compression performance of reversible integer wavelet transforms. The projection technique is developed and used to predict a wavelet transform coefficient as a linear combination of other wavelet transform coefficients. It yields optimal fixed prediction steps for lifting-based wavelet transforms and unifies many wavelet-based lossless image compression results found in the literature. Additionally, the projection technique is used in an adaptive prediction scheme that varies the final prediction step of the lifting-based transform based on a modeling context. Compared to current fixed and adaptive lifting-based transforms, the projection technique produces improved reversible integer wavelet transforms with superior lossless compression performance. It also provides a generalized framework that explains and unifies many previous results in wavelet-based lossless image compression.

Nonlinear wavelet transforms for image coding via lifting

We investigate central issues such as invertibility, stability, synchronization, and frequency characteristics for nonlinear wavelet transforms built using the lifting framework. The nonlinearity comes from adaptively choosing between a class of linear predictors within the lifting framework. We also describe how earlier families of nonlinear filter banks can be extended through the use of prediction functions operating on a causal neighborhood of pixels. Preliminary compression results for model and real-world images demonstrate the promise of our techniques.

Optimization and implementation of the integer wavelet transform for image coding

This paper deals with the design and implementation of an image transform coding algorithm based on the integer wavelet transform (IWT). First of all, criteria are proposed for the selection of optimal factorizations of the wavelet filter polyphase matrix to be employed within the lifting scheme. The obtained results lead to the IWT implementations with very satisfactory lossless and lossy compression performance. Then, the effects of finite precision representation of the lifting coefficients on the compression performance are analyzed, showing that, in most cases, a very small number of bits can be employed for the mantissa keeping the performance degradation very limited. Stemming from these results, a VLSI architecture is proposed for the IWT implementation, capable of achieving very high frame rates with moderate gate complexity.

Wavelets and imaging informatics: a review of the literature

Modern medicine is a field that has been revolutionized by the emergence of computer and imaging technology. It is increasingly difficult, however, to manage the ever-growing enormous amount of medical imaging information available in digital formats. Numerous techniques have been developed to make the imaging information more easily accessible and to perform analysis automatically. Among these techniques, wavelet transforms have proven prominently useful not only for biomedical imaging but also for signal and image processing in general. Wavelet transforms decompose a signal into frequency bands, the width of which are determined by a dyadic scheme. This particular way of dividing frequency bands matches the statistical properties of most images very well. During the past decade, there has been active research in applying wavelets to various aspects of imaging informatics, including compression, enhancements, analysis, classification, and retrieval. This review represents a survey of the most significant practical and theoretical advances in the field of wavelet-based imaging informatics.

Integer wavelet transform for embedded lossy to lossless image compression

The use of the discrete wavelet transform (DWT) for embedded lossy image compression is now well established. One of the possible implementations of the DWT is the lifting scheme (LS). Because perfect reconstruction is granted by the structure of the LS, nonlinear transforms can be used, allowing efficient lossless compression as well. The integer wavelet transform (IWT) is one of them. This is an interesting alternative to the DWT because its rate-distortion performance is similar and the differences can be predicted. This topic is investigated in a theoretical framework. A model of the degradations caused by the use of the IWT instead of the DWT for lossy compression is presented. The rounding operations are modeled as additive noise. The noise are then propagated through the LS structure to measure their impact on the reconstructed pixels. This methodology is verified using simulations with random noise as input. It predicts accurately the results obtained using images compressed by the well-known EZW algorithm. Experiment are also performed to measure the difference in terms of bit rate and visual quality. This allows to a better understanding of the impact of the IWT when applied to lossy image compression.

Lossless image compression based on optimal prediction, adaptive lifting, and conditional arithmetic coding

The optimal predictors of a lifting scheme in the general n-dimensional case are obtained and applied for the lossless compression of still images using first quincunx sampling and then simple row-column sampling. In each case, the efficiency of the linear predictors is enhanced nonlinearly. Directional postprocessing is used in the quincunx case, and adaptive-length postprocessing in the row-column case. Both methods are seen to perform well. The resulting nonlinear interpolation schemes achieve extremely efficient image decorrelation. We further investigate context modeling and adaptive arithmetic coding of wavelet coefficients in a lossless compression framework. Special attention is given to the modeling contexts and the adaptation of the arithmetic coder to the actual data. Experimental evaluation shows that the best of the resulting coders produces better results than other known algorithms for multiresolution-based lossless image coding.

Multifractal wavelet filter of natural images

Natural images are characterized by the multiscaling properties of their contrast gradient, in addition to their power spectrum. In this Letter we show that those properties uniquely define an intrinsic wavelet and present a suitable technique to obtain it from an ensemble of images. Once this wavelet is known, images can be represented as expansions in the associated wavelet basis. The resulting code has the remarkable properties that it separates independent features at different resolution level, reducing the redundancy, and remains essentially unchanged under changes in the power spectrum. The possible generalization of this representation to other systems is discussed.

Three-dimensional image compression with integer wavelet transforms

A three-dimensional (3-D) image-compression algorithm based on integer wavelet transforms and zerotree coding is presented. The embedded coding of zerotrees of wavelet coefficients (EZW) algorithm is extended to three dimensions, and context-based adaptive arithmetic coding is used to improve its performance. The resultant algorithm, 3-D CB-EZW, efficiently encodes 3-D image data by the exploitation of the dependencies in all dimensions, while enabling lossy and lossless decompression from the same bit stream. Compared with the best available two-dimensional lossless compression techniques, the 3-D CB-EZW algorithm produced averages of 22%, 25%, and 20% decreases in compressed file sizes for computed tomography, magnetic resonance, and Airborne Visible Infrared Imaging Spectrometer images, respectively. The progressive performance of the algorithm is also compared with other lossy progressive-coding algorithms.

Reversible integer-to-integer wavelet transforms for image compression: performance evaluation and analysis

In the context of image coding, a number of reversible integer-to-integer wavelet transforms are compared on the basis of their lossy compression performance, lossless compression performance, and computational complexity. Of the transforms considered, several were found to perform particularly well, with the best choice for a given application depending on the relative importance of the preceding criteria. Reversible integer-to-integer versions of numerous transforms are also compared to their conventional (i.e., nonreversible real-to-real) counterparts for lossy compression. At low bit rates, reversible integer-to-integer and conventional versions of transforms were found to often yield results of comparable quality. Factors affecting the compression performance of reversible integer-to-integer wavelet transforms are also presented, supported by both experimental data and theoretical arguments.

Scalable image coding using reversible integer wavelet transforms

Reversible integer wavelet transforms allow both lossless and lossy decoding using a single bitstream. We present a new fully scalable image coder and investigate the lossless and lossy performance of these transforms in the proposed coder. The lossless compression performance of the presented method is comparable to JPEG-LS. The lossy performance is quite competitive with other efficient lossy compression methods.

Orientation-sensitive interpolative pyramids for lossless and progressive image coding

This paper presents a modified JPEG coder that is applied to the compression of mixed documents (containing text, natural images, and graphics) for printing purposes. The modified JPEG coder proposed in this paper takes advantage of the distinct perceptually significant regions in these documents to achieve higher perceptual quality than the standard JPEG coder. The region-adaptivity is performed via classified thresholding being totally compliant with the baseline standard. A computationally efficient classification algorithm is presented, and the improved performance of the classified JPEG coder is verified.

Lossless quantization of Hadamard transform coefficients

This paper shows that an n x 1 integer vector can be exactly recovered from its Hadamard transform coefficients, even when 0.5 n log(2)(n) of the (less significant) bits of these coefficients are removed. The paper introduces a fast "lossless" dequantization algorithm for this purpose. To investigate the usefulness of the procedure in data compression, the paper investigates an embedded block image coding technique called the "LHAD" based on the algorithm. The results show that lossless compression ratios close to the state of the art can be achieved, but that techniques such as CALIC and S+P still perform better.

Adaptive polyphase subband decomposition structures for image compression

Subband decomposition techniques have been extensively used for data coding and analysis. In most filter banks, the goal is to obtain subsampled signals corresponding to different spectral regions of the original data. However, this approach leads to various artifacts in images having spatially varying characteristics, such as images containing text, subtitles, or sharp edges. In this paper, adaptive filter banks with perfect reconstruction property are presented for such images. The filters of the decomposition structure which can be either linear or nonlinear vary according to the nature of the signal. This leads to improved image compression ratios. Simulation examples are presented.