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PubMed1998

Digital echocardiographic laboratory: where do we stand?

The transition to an all-digital echocardiographic laboratory has been slow despite the advantages of digital echocardiography, the dramatic improvement in computer technology, and the acceptance by all major vendors and professional organizations of the Digital Imaging and Communications in Medicine image-formatting standard. This review article examines some current issues in digital echocardiography, including types of digital output, disk versus network exchange, digital and clinical compression techniques, and the choice of image storage format. Although specific exceptions exist, we conclude that the optimal solution will be one based on a network exchange of formatted images that follow the Digital Imaging and Communications in Medicine standard, now available from several manufacturers. For a clinical interpretation, modest compression with the Joint Photography Expert Group algorithm appears acceptable, though possible enhancements to this compression standard are also discussed. We hope this review will enable the echocardiographic community to make more intelligent choices as digital storage and transmission products become available in the marketplace.

Algorithms↗
PubMed1991

Automated feature detection in digital images of skin.

This paper presents a new approach to the detection of cutaneous features such as pigmented moles, pores and hair in digital images. We show that by considering the digitized image to be a 3-D terrain with brightness being height, common skin features appear as 'pits' in the terrain. Pits contain a great deal of information about local features in a form that can be easily extracted and analyzed. Pigmented lesions of clinical interest typically have pit characteristics which can be used to separate them from other features. We show empirically that, by creating a statistical database, on the average over 99% of pits can be classified correctly after only a few training images are established for a subject. When attempting to detect pigmented lesions, mean sensitivity ranges from 78% to 98%, depending on imaging conditions and the classification algorithm used. Using image compression, the speed of this screening technique is shown to be increased by a factor of 4 without loss of sensitivity.

Algorithms↗
PubMed2005

Principles of digital dynamic-range compression.

This article provides an overview of dynamic-range compression in digital hearing aids. Digital technology is becoming increasingly common in hearing aids, particularly because of the processing flexibility it offers and the opportunity to create more-effective devices. The focus of the paper is on the algorithms used to build digital compression systems. Of the various approaches that can be used to design a digital hearing aid, this paper considers broadband compression, multi-channel filter banks, a frequency-domain compressor using the FFT, the side-branch design that separates the filtering operation from the frequency analysis, and the frequency-warped version of the side-branch approach that modifies the analysis frequency spacing to more closely match auditory perception. Examples of the compressor frequency resolution, group delay, and compression behavior are provided for the different design approaches.

Algorithms↗
PubMed2005

Contextual encoding in uniform and adaptive mesh-based lossless compression of MR images.

We propose and evaluate a number of novel improvements to the mesh-based coding scheme for 3-D brain magnetic resonance images. This includes: 1) elimination of the clinically irrelevant background leading to meshing of only the brain part of the image; 2) content-based (adaptive) mesh generation using spatial edges and optical flow between two consecutive slices; 3) a simple solution for the aperture problem at the edges, where an accurate estimation of motion vectors is not possible; and 4) context-based entropy coding of the residues after motion compensation using affine transformations. We address only lossless coding of the images, and compare the performance of uniform and adaptive mesh-based schemes. The bit rates achieved (about 2 bits per voxel) by these schemes are comparable to those of the state-of-the-art three-dimensional (3-D) wavelet-based schemes. The mesh-based schemes have been shown to be effective for the compression of 3-D brain computed tomography data also. Adaptive mesh-based schemes perform marginally better than the uniform mesh-based methods, at the expense of increased complexity.

Algorithms↗
PubMed1999

New techniques for DNA sequence classification.

DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.

Algorithms↗
PubMed2005

Wavelet coding of volumetric medical images for high throughput and operability.

This paper presents a new three-dimensional (3-D) wavelet-based scalable lossless coding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3-D dyadic wavelet transform tailored to volumetric medical images and an optimized Rice code of very low complexity.

Algorithms↗
PubMed2006

Mammogram registration: a phantom-based evaluation of compressed breast thickness variation effects.

The temporal comparison of mammograms is complex; a wide variety of factors can cause changes in image appearance. Mammogram registration is proposed as a method to reduce the effects of these changes and potentially to emphasize genuine alterations in breast tissue. Evaluation of such registration techniques is difficult since ground truth regarding breast deformations is not available in clinical mammograms. In this paper, we propose a systematic approach to evaluate sensitivity of registration methods to various types of changes in mammograms using synthetic breast images with known deformations. As a first step, images of the same simulated breasts with various amounts of simulated physical compression have been used to evaluate a previously described nonrigid mammogram registration technique. Registration performance is measured by calculating the average displacement error over a set of evaluation points identified in mammogram pairs. Applying appropriate thickness compensation and using a preferred order of the registered images, we obtained an average displacement error of 1.6 mm for mammograms with compression differences of 1-3 cm. The proposed methodology is applicable to analysis of other sources of mammogram differences and can be extended to the registration of multimodality breast data.

Algorithms↗
PubMed2005

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.

Algorithms↗
PubMedDate not supplied

Joint thresholding and quantizer selection for compression of medical ultrasound images in the wavelet domain.

This paper introduces a simple and efficient technique for compression of medical ultrasound (US) images in the wavelet domain. The statistics of subband wavelet coefficients are modelled using the generalized Gaussian distribution (GGD). By exploiting these statistics, a uniform scalar quantizer is designed which adapts very well to the changing statistics of the signal across various subbands and scales. To increase the quantization performance, a threshold is chosen adaptively to zero-out the insignificant wavelet coefficients in the detail subbands before quantization. A distinctive feature of the proposed technique is that it unifies the two approaches to image adaptive coding: rate-distortion (R-D) optimized quantizer selection and R-D optimal thresholding, in order to increase the compression performance of the coder. The operational R-D criterion used for joint optimization is derived in the minimum description length (MDL) framework. The experimental results show that the joint R-D optimization leads to significant improvement in the compression performance of the proposed coder, named JTQ-WV, over the best state-of-the-art image coder, SPIHT. For example, the coding of US images at 0.25 bpp by JTQ-WV yields a PSNR gain of 1.0 dB over the benchmark SPIHT.

Algorithms↗
PubMed1997

Digital radiographic imaging transfer: comparison with plain radiographs.

Advances in digital imaging and computer display technology have allowed development of clinical teleradiographic systems. There are limited data assessing the effectiveness of such systems when applied to urologic pathology. In an effort to appraise the effectiveness of teleradiology in identifying renal calculi, the accuracy of findings on transmitted radiographic images were compared with those made when viewing the actual plain film. Plain films (KUB) were obtained from 26 patients who presented to the radiology department to rule out urinary calculous disease. The films were digitalized by a radiograph scanner into ARCNEMA-2 file format, compressed by a NASA algorithm, and transferred via a 28.8-kbps modern over standard telephone lines to a remote section 25 miles away, where they were decompressed and viewed on a 1600 x 1200-pixel monitor. Two attending urologists and two endourologic fellows were randomized to read either the transmitted image or the original radiograph with minimal clinical history provided. Of the 26 plain radiographic films, 24 were correctly interpreted by the fellows and 25 by the attending physicians (92% and 96% accuracy, respectively) for a total accuracy of 94% with no statistical difference (p = 0.16). After compression, all but one of the digital images were transferred successfully. The attending physicians correctly interpreted 24 of the 25 digital images (96%), whereas the fellows were correct on 21 interpretations (84%), resulting in a total 90% accuracy with a significant difference between the groups (p < or = 0.04). Overall, no statistical difference between the interpretations of the plain film and the digital image was revealed (p = 0.21). Using available technology, KUB images can be transmitted to a remote site, and the location of a stone can be determined correctly. Higher accuracy is demonstrated by experienced surgeons.

Algorithms↗
PubMed2006

An overview of digital compression of medical images: can we use lossy image compression in radiology?

The increasing volume of data generated by new imaging modalities such as multislice computed tomography scanners and magnetic resonance imaging justifies the use of lossy compression techniques to decrease the cost of storage and improve the efficiency of transmission over networks for teleradiology or for access to electronic patient records. We summarize here the most commonly used compression techniques and compare their main features. Having conducted an extensive literature review, we present a range of average compression ratios for different modalities and body parts. This article lays the groundwork for further evaluation with standardized statistical methods to ultimately elaborate acceptable compression guidelines.

Algorithms↗
PubMed2004

Automatic quality control for wavelet-based compression of volumetric medical images using distortion-constrained adaptive vector quantization.

The enormous data of volumetric medical images (VMI) bring a transmission and storage problem that can be solved by using a compression technique. For the lossy compression of a very long VMI sequence, automatically maintaining the diagnosis features in reconstructed images is essential. The proposed wavelet-based adaptive vector quantizer incorporates a distortion-constrained codebook replenishment (DCCR) mechanism to meet a user-defined quality demand in peak signal-to-noise ratio. Combining a codebook updating strategy and the well-known set partitioning in hierarchical trees (SPIHT) technique, the DCCR mechanism provides an excellent coding gain. Experimental results show that the proposed approach is superior to the pure SPIHT and the JPEG2000 algorithms in terms of coding performance. We also propose an iterative fast searching algorithm to find the desired signal quality along an energy-quality curve instead of a traditional rate-distortion curve. The algorithm performs the quality control quickly, smoothly, and reliably.

Algorithms↗
PubMed2000

Compression of real time volumetric echocardiographic data using modified SPIHT based on the three-dimensional wavelet packet transform.

Real-time three-dimensional echocardiography has been introduced to provide improved quantification and description of cardiac function. Data compression is desired to allow efficient storage and improve data transmission. Previous work has suggested improved results utilizing wavelet transforms in the compression of medical data including 2D echocardiogram. Set partitioning in hierarchical trees (SPIHT) was extended to compress volumetric echocardiographic data by modifying the algorithm based on the three-dimensional wavelet packet transform. A compression ratio of at least 40:1 resulted in preserved image quality.

Aerospace Medicine↗
PubMed2006

An RBF-based compression method for image-based relighting.

In image-based relighting, a pixel is associated with a number of sampled radiance values. This paper presents a two-level compression method. In the first level, the plenoptic property of a pixel is approximated by a spherical radial basis function (SRBF) network. That means that the spherical plenoptic function of each pixel is represented by a number of SRBF weights. In the second level, we apply a wavelet-based method to compress these SRBF weights. To reduce the visual artifact due to quantization noise, we develop a constrained method for estimating the SRBF weights. Our proposed approach is superior to JPEG, JPEG2000, and MPEG. Compared with the spherical harmonics approach, our approach has a lower complexity, while the visual quality is comparable. The real-time rendering method for our SRBF representation is also discussed.

Algorithms↗
PubMed2006

Multiresolution wavelet analysis for efficient analysis, compression and remote display of long-term physiological signals.

Increased inter-equipment connectivity coupled with advances in Web technology allows ever escalating amounts of physiological data to be produced, far too much to be displayed adequately on a single computer screen. The consequence is that large quantities of insignificant data will be transmitted and reviewed. This carries an increased risk of overlooking vitally important transients. This paper describes a technique to provide an integrated solution based on a single algorithm for the efficient analysis, compression and remote display of long-term physiological signals with infrequent short duration, yet vital events, to effect a reduction in data transmission and display cluttering and to facilitate reliable data interpretation. The algorithm analyses data at the server end and flags significant events. It produces a compressed version of the signal at a lower resolution that can be satisfactorily viewed in a single screen width. This reduced set of data is initially transmitted together with a set of 'flags' indicating where significant events occur. Subsequent transmissions need only involve transmission of flagged data segments of interest at the required resolution. Efficient processing and code protection with decomposition alone is novel. The fixed transmission length method ensures clutter-less display, irrespective of the data length. The flagging of annotated events in arterial oxygen saturation, electroencephalogram and electrocardiogram illustrates the generic property of the algorithm. Data reduction of 87% to 99% and improved displays are demonstrated.

Algorithms↗
PubMed2006

Wavelet-based scalable L-infinity-oriented compression.

Among the different classes of coding techniques proposed in literature, predictive schemes have proven their outstanding performance in near-lossless compression. However, these schemes are incapable of providing embedded L(infinity)-oriented compression, or, at most, provide a very limited number of potential L(infinity) bit-stream truncation points. We propose a new multidimensional wavelet-based L(infinity)-constrained scalable coding framework that generates a fully embedded L(infinity)-oriented bit stream and that retains the coding performance and all the scalability options of state-of-the-art L2-oriented wavelet codecs. Moreover, our codec instantiation of the proposed framework clearly outperforms JPEG2000 in L(infinity) coding sense.

Algorithms↗
PubMed2006

On the use of 2-D coding techniques for ECG signals.

This paper discusses the convenience of using two-dimensional (2-D) coding techniques for the compression of electrocardiogram (ECG) signals. These signals present a very clear periodicity that can be exploited by the use of a 2-D time/frequency transform to decorrelate it as much as possible. A brief theoretical approach is given to justify the use of this technique, and a comparison is made between a 2-D and a one-dimensional (1-D) uniform quantization scenarios. The influence of the error as well as the frame size on the estimation of the fundamental period is studied.

Algorithms↗
PubMed2006

On the use of context-weighting in lossless bilevel image compression.

We present a context-weighting algorithm that adaptively weights in real-time three-context models based on their relative accuracy. It can automatically select the better model over different regions of an image, producing better probability estimates than using either one of these models exclusively. Combined with the previously proposed block arithmetic coder for image compression (BACIC), the overall performance is slightly better than JBIG for the eight CCITT business-type test images, outperforms JBIG by 13.8% on halftone images, and by 17.5% for compounded images containing both text and halftones. Furthermore, users no longer need to select models as in JBIG and BACIC to get the better performance.

Algorithms↗