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PubMed2002

Evaluation of irreversible JPEG compression for a clinical ultrasound practice.

A prior ultrasound study indicated that images with low to moderate levels of JPEG and wavelet compression were acceptable for diagnostic purposes. The purpose of this study is to validate this prior finding using the Joint Photographic Experts Group (JPEG) baseline compression algorithm, at a compression ratio of approximately 10:1, on a sufficiently large number of grayscale and color ultrasound images to attain a statistically significant result. The practical goal of this study is to determine if it is feasible for radiologists to use irreversibly compressed images as an integral part of the day to day ultrasound practice (ie, perform primary diagnosis with, and store irreversibly compressed images in the ultrasound PACS archive). In this study, 5 Radiologists were asked to review 300 grayscale and color static ultrasound images selected from 4 major anatomic groups. Each image was compressed and decompressed using the JPEG baseline compression algorithm at a fixed quality factor resulting in an average compression ratio of approximately 9:1. The images were presented in pairs (original and compressed) in a blinded fashion on a PACS workstation in the ultrasound reading areas, and radiologists were asked to pick which image they preferred in terms of diagnostic utility and their degree of certainty (on a scale from 1 to 4). Of the 1499 total readings, 50.17% (95% confidence intervals at 47.6%, and 52.7%) indicated a preference for the original image in the pair, and 49.83% (95% confidence intervals at 47.3%, and 52.0%) indicated a preference for the compressed image. These findings led the authors to conclude that static color and gray-scale ultrasound images compressed with JPEG at approximately 9:1 are statistically indistinguishable from the originals for primary diagnostic purposes. Based on the authors laboratory experience with compression and the results of this and other prior studies, JPEG compression is now being applied to all ultrasound images in the authors' radiology practice before reading. No image quality-related issues have been encountered after 12 months of operation (approximately 48000 examinations).

Algorithms↗
PubMed2000

Optimal autoregressive model based medical image compression using genetic algorithm.

Image compression is the process of removal of redundant data in order to achieve savings in storage and communication costs. A new method for predictive coding of images is proposed using two-dimensional multiplicative autoregressive models. Genetic algorithm is implemented to compute autoregressive parameters. Comparison with other multiplicative autoregressive model based image compression method has also been reported here.

Algorithms↗
PubMed2000

ECG signal compression using analysis by synthesis coding.

In this paper, an elecrocardiogram (ECG) compression algorithm, called analysis by synthesis ECG compressor (ASEC), is introduced. The ASEC algorithm is based on analysis by synthesis coding, and consists of a beat codebook, long and short-term predictors, and an adaptive residual quantizer. The compression algorithm uses a defined distortion measure in order to efficiently encode every heartbeat, with minimum bit rate, while maintaining a predetermined distortion level. The compression algorithm was implemented and tested with both the percentage rms difference (PRD) measure and the recently introduced weighted diagnostic distortion (WDD) measure. The compression algorithm has been evaluated with the MIT-BIH Arrhythmia Database. A mean compression rate of approximately 100 bits/s (compression ratio of about 30:1) has been achieved with a good reconstructed signal quality (WDD below 4% and PRD below 8%). The ASEC was compared with several well-known ECG compression algorithms and was found to be superior at all tested bit rates. A mean opinion score (MOS) test was also applied. The testers were three independent expert cardiologists. As in the quantitative test, the proposed compression algorithm was found to be superior to the other tested compression algorithms.

Algorithms↗
PubMed2004

The impact of image compression on diagnostic quality of digital images for detection of chemically-induced periapical lesions.

OBJECTIVES: To test the hypothesis that there is no significant difference in the detectability of chemically-induced periapical lesions between a non-compressed image and one subjected to a Joint Photographic Experts Group (JPEG) lossy compression technique at a ratio of 23:1 or less. METHOD: Chemically-induced periapical lesions were created by placing a solution of 70% perchloric acid at the apex of extracted teeth in 13 human jaw cadaver specimens. Acid was applied in seven incremental time periods from 0-32 h. Extracted teeth were replaced in the socket and images were made using the Schick CDR digital sensor. Using a JPEG lossy compression algorithm, five compression ratios of 2:1, 14:1, 23:1, 28:1 and 47:1 were applied to the images. Images were viewed three times by three observers who ranked the presence or absence of a lesion at three sites, the mesial area, distal area and apex of the tooth, on a 5-point confidence scale. Intraobserver and interobserver agreement and agreement between the compressed and the original images were assessed with intraclass correlation coefficients (ICCs). RESULTS: Overall ICCs for measuring intraobserver agreement using all images were 0.77, 0.84, and 0.50 for the three observers, respectively. The overall ICC for assessing agreement between observers was 0.57. There was no significant difference (P>0.05) between compressed and original images for any site at compression ratios of 2:1, 23:1 and 28:1. There were significant differences for a compression ratio of 47:1. CONCLUSION: JPEG compression does not impact detectability of artificial periapical lesions at low and moderate compression ratios up to and including 28:1.

Algorithms↗
PubMed2006

Rate distortion optimal bit allocation methods for volumetric data using JPEG 2000.

Computer modeling programs that generate three-dimensional (3-D) data on fine grids are capable of generating very large amounts of information. These data sets, as well as 3-D sensor/measured data sets, are prime candidates for the application of data compression algorithms. A very flexible and powerful compression algorithm for imagery data is the newly released JPEG 2000 standard. JPEG 2000 also has the capability to compress volumetric data, as described in Part 2 of the standard, by treating the 3-D data as separate slices. As a decoder standard, JPEG 2000 does not describe any specific method to allocate bits among the separate slices. This paper proposes two new bit allocation algorithms for accomplishing this task. The first procedure is rate distortion optimal (for mean squared error), and is conceptually similar to postcompression rate distortion optimization used for coding codeblocks within JPEG 2000. The disadvantage of this approach is its high computational complexity. The second bit allocation algorithm, here called the mixed model (MM) approach, mathematically models each slice's rate distortion curve using two distinct regions to get more accurate modeling at low bit rates. These two bit allocation algorithms are applied to a 3-D Meteorological data set. Test results show that the MM approach gives distortion results that are nearly identical to the optimal approach, while significantly reducing computational complexity.

Algorithms↗
PubMed1996

Lossless medical image compression by multilevel decomposition.

Lossless image coding is important for medical image compression because any information loss or error caused by the image compression process could affect clinical diagnostic decisions. This paper proposes a lossless compression algorithm for application to medical images that have high spatial correlation. The proposed image compression algorithm uses a multi-level decomposition scheme in conjunction with prediction and classification. In this algorithm, an image is divided into four subimages by subsampling. One subimage is used as a reference to predict the other three subimages. The prediction errors of the three subimages are classified into two or three groups by the characteristics of the reference subimage, and the classified prediction errors are encoded by entropy coding with corresponding code words. These subsampling and classified entropy coding procedures are repeated on the reference subimage in each level, and the reference subimage in the last repetition is encoded by conventional differential pulse code modulation and entropy coding. To verify this proposed algorithm, it was applied to several chest radiographs and computed tomography and magnetic resonance images, and the results were compared with those from well-known lossless compression algorithms.

Algorithms↗
PubMed1993

A new method for computed tomography image compression using adjacent slice data.

RATIONALE AND OBJECTIVES: The authors developed and subjectively evaluated an interslice compression algorithm that explores the redundancy among adjacent slices of an x-ray computed tomography (CT) scan. This algorithm has been compared to an intraslice compression algorithm based on the two-dimensional discrete cosine transform. METHODS: Nine x-ray CT head images from three patients were compressed with this interslice method at compression ratios of 5:1, 10:1, and 15:1. The same images were also compressed with the intraslice method at the same ratios. Six radiologists judged quality of randomly selected compressed and decompressed images compared to that of the originals. The evaluation data were analyzed statistically with the analysis of variance and Tukey's multiple comparison. Kappa-like statistics (Williams index and O'Connell and Dobson indexes) were also calculated to measure the agreement among readers beyond the amount expected by chance. RESULTS: The interslice coding algorithm showed significantly better quality than the intraslice method at significance level 0.05, even though there was no difference in the objective distortion measure (signal-to-noise ratio). Also, the quality of 10:1 compressed images with the interslice coding algorithm was not significantly different from that of the originals at level 0.05. While large variations in agreement occurred among readers, the overall agreement was statistically significant. CONCLUSIONS: By using adjacent slice information in compressing x-ray CT images, significantly better quality in compressed and decompressed images was achieved. While 10:1 compressed images with the interslice algorithm were not significantly different from the originals in quality at level 0.05, effect on diagnostic accuracy remains to be investigated.

Algorithms↗
PubMed2004

An efficient re-indexing algorithm for color-mapped images.

The efficiency of lossless compression algorithms for fixed-palette images (indexed images) may change if a different indexing scheme is adopted. Many lossless compression algorithms adopt a differential-predictive approach. Hence, if the spatial distribution of the indexes over the image is smooth, greater compression ratios may be obtained. Because of this, finding an indexing scheme that realizes such a smooth distribution is a relevant issue. Obtaining an optimal re-indexing scheme is suspected to be a hard problem and only approximate solutions have been provided in literature. In this paper, we restate the re-indexing problem as a graph optimization problem: an optimal re-indexing corresponds to the heaviest Hamiltonian path in a weighted graph. It follows that any algorithm which finds a good approximate solution to this graph-theoretical problem also provides a good re-indexing. We propose a simple and easy-to-implement approximation algorithm to find such a path. The proposed technique compares favorably with most of the algorithms proposed in literature, both in terms of computational complexity and of compression ratio.

Algorithms↗
PubMedDate not supplied

The effect on speech intelligibility of varying compression time constants in a digital hearing aid.

The identification of nonsense syllables in quiet and in three types of background (babble, cafeteria and single female speaker) was measured using four hearing aid compression algorithms differing in attack and release time constants, and using linear amplification. The speech level was always 65 dB SPL. The compression algorithms, which were implemented in a Phonak Claro ITE hearing aid, were: (1) 'very fast'--the attack time was 8 ms and the release time was 32 ms, for all 20 channels; (2) 'slow-fast'--the attack and release times decreased from 500 ms for low frequencies to about lOOms for high frequencies; (3) 'fast-slow'-the attack and release times increased from about 50ms for low frequencies to 500 ms for high frequencies; and (4) 'slow + fast'-a very slow-acting gain control signal was combined with a fast-acting gain control signal, for each channel in a 10-channel system. Acoustical stimuli were presented monaurally via a circumaural headphone mounted over the hearing aid. The linear condition did not use the Claro aid; instead, the signal was digitally filtered to implement the Cambridge formula prior to delivery via the earphone. Five subjects with moderate sensorineural hearing loss were tested in a counter-balanced order across conditions. In quiet, performance was best for linear amplification and worst for the slow + fast algorithm. In the presence of background sounds, the highest scores were obtained with the linear-gain Cambridge formula implemented via headphones; a supplementary experiment suggested that this was due to the greater high-frequency gain resulting from the use of this formula. No significant differences were found between scores for the different compression algorithms. We conclude that the intelligibility of speech at a fixed level, presented in background sounds, is not markedly affected by rather substantial variations of the time constants in a multichannel compression system.

Aged↗
PubMedDate not supplied

Adaptive compression of the ambulatory electrocardiogram.

Previous use of the MIT/BIH arrhythmia database, on analog tape, to investigate compression of ambulatory ECG data by average beat subtraction, residual differencing, and Huffman coding of the residuals had shown that with a quantization level of 35 mu V and a sample rate of 100 samples per second, it was possible to store ECG data with average data rates of 174 bits per second (bps), but because of the variation in ECG signals, data rates for different records ranged from 144 bps to 230 bps. In a practical storage system, it is desirable to fix the maximum data rate and store data with a minimum of distortion. For this study the previous compression algorithm was modified to adapt its quantization level to different ECG signal conditions. Two adaptation strategies were investigated. Both adapt the quantization-step size according to the number of bytes required for storing the coded signal, beat arrival times, and beat classifications. The new compression algorithm was tested with data from the MIT/BIH database on CD ROM. With the more successful of the two strategies, the adaptive compression algorithm stored MIT/BIH records with a difference of only 0.8 bps between the record with the highest data rate and the record with the lowest data rate. The average data rate for the entire database was 193.3 bps. Signal-to-compression noise ratios varied from record to record and varied over time for a given record. Average signal to compression noise ratios varied from 26.82 to 532.83.

Algorithms↗
PubMed2003

Visually lossless threshold determination for microcalcification detection in wavelet compressed mammograms.

The aim of this study was to determine the visually lossless threshold of a wavelet-based compression algorithm in case of microcalcification cluster detection in mammography. The threshold was determined by means of observer performance using a set of digitized mammograms. In addition, the transfer characteristics of the compression algorithm were assessed by means of image-quality parameters using computer-generated test images. The observer performance study was based on rating performed by four independent radiologists, who reviewed 68 mammograms, from the Digital Database for Screening Mammography (DDSM), at six different compression ratios. Receiver operating characteristics (ROC) analysis was performed on observers' responses and the area under ROC curve (A(z)) was calculated at each compression ratio for each observer. The parameters used for assessment of transfer characteristics of the compression algorithm were input/output response, noise, high-contrast response, and low-contrast-detail response. The computer-generated test image, used for this assessment, mimicked mammographic image characteristics (pixel size, pixel depth, and noise) as well as microcalcification characteristics (size and contrast). The ROC analysis for microcalcification cluster detection indicated a threshold at compression ratio 40:1, as Student's t-test shows statistically significant differences in A(z) values (p<0.05) for compression ratios 70:1 and 100:1. Observers' grading of mammogram quality lowers this threshold at 25:1. Low-contrast-detail detectability in the transfer characteristics study indicate a threshold of 35:1, whereas non-perceptibility of image-quality-parameters degradation lowers this threshold to 30:1. The ROC and transfer characteristics analysis provided comparable thresholds, indicating the potential use of the latter in limiting the target range of compression ratios for subsequent observer studies.

Aged↗
PubMed1997

Wavelet transform in electrocardiography--data compression.

An application of the wavelet transform to electrocardiography is described in the paper. The transform is used as a first stage of a lossy compression algorithm for efficient coding of rest ECG signals. The proposed technique is based on the decomposition of the ECG signal into a set of basic functions covering the time-frequency domain. Thus, non-stationary character of ECG data is considered. Some of the time-frequency signal components are removed because of their low influence to signal characteristics. Resulting components are efficiently coded by quantization, composition into a sequence of coefficients and compression by a run-length coder and a entropic Huffman coder. The proposed wavelet-based compression algorithm can compress data to average code length about 1 bit/sample. The algorithm can be also implemented to a real-time processing system when wavelet transform is computed by fast linear filters described in the paper.

Acoustics↗
PubMed1994

Application of wavelet compression to digitized radiographs.

OBJECTIVE: Image data compression is an enabling technology for teleradiology and picture archive and communication systems. Compression decreases the time and cost of image transmission and the requirements for image storage. Wavelets, discovered in 1987, constitute a new compression technique that has been described in radiologic publications but, to our knowledge, no previous studies of its use have been reported. The purpose of this study was to demonstrate the application of wavelet-based compression technology to digitized radiographs. MATERIALS AND METHODS: Twelve radiographs with abnormal findings were digitized, compressed, and decompressed by using a new wavelet-based lossy compression algorithm. Images were compressed at ratios from 10:1 to 60:1. Seven board-certified radiologists reviewed images on a two-headed, high-resolution (2K x 2K) diagnostic workstation. Paired original and compressed/decompressed images were presented in random order. Reviewers adjusted contrast and magnification to judge whether image degradation was present, and if so, whether it was of diagnostic significance. Quantitative error measures were tabulated. RESULTS: Reviewers found no clinically relevant degradation below a compression ratio of 30:1. Skeletal radiographs appeared more sensitive to compression than did chest or abdominal radiographs, but the trend was not statistically significant. Quantitative error measures increased gradually with compression ratio. CONCLUSION: On the basis of subjective assessment of image quality and the computational efficiency of the algorithm, wavelet-base techniques appear promising for the compression of digitized radiographs. The results of this initial experience can be used to design appropriate observer performance studies.

Adult↗
PubMed2006

An ultra-low-power image compressor for capsule endoscope.

BACKGROUND: Gastrointestinal (GI) endoscopy has been popularly applied for the diagnosis of diseases of the alimentary canal including Crohn's Disease, Celiac disease and other malabsorption disorders, benign and malignant tumors of the small intestine, vascular disorders and medication related small bowel injury. The wireless capsule endoscope has been successfully utilized to diagnose diseases of the small intestine and alleviate the discomfort and pain of patients. However, the resolution of demosaicked image is still low, and some interesting spots may be unintentionally omitted. Especially, the images will be severely distorted when physicians zoom images in for detailed diagnosis. Increasing resolution may cause significant power consumption in RF transmitter; hence, image compression is necessary for saving the power dissipation of RF transmitter. To overcome this drawback, we have been developing a new capsule endoscope, called GICam. METHODS: We developed an ultra-low-power image compression processor for capsule endoscope or swallowable imaging capsules. In applications of capsule endoscopy, it is imperative to consider battery life/performance trade-offs. Applying state-of-the-art video compression techniques may significantly reduce the image bit rate by their high compression ratio, but they all require intensive computation and consume much battery power. There are many fast compression algorithms for reducing computation load; however, they may result in distortion of the original image, which is not good for use in the medical care. Thus, this paper will first simplify traditional video compression algorithms and propose a scalable compression architecture. CONCLUSION: As the result, the developed video compressor only costs 31 K gates at 2 frames per second, consumes 14.92 mW, and reduces the video size by 75% at least.

Algorithms↗
PubMed2005

Compound image compression for real-time computer screen image transmission.

We present a compound image compression algorithm for real-time applications of computer screen image transmission. It is called shape primitive extraction and coding (SPEC). Real-time image transmission requires that the compression algorithm should not only achieve high compression ratio, but also have low complexity and provide excellent visual quality. SPEC first segments a compound image into text/graphics pixels and pictorial pixels, and then compresses the text/graphics pixels with a new lossless coding algorithm and the pictorial pixels with the standard lossy JPEG, respectively. The segmentation first classifies image blocks into picture and text/graphics blocks by thresholding the number of colors of each block, then extracts shape primitives of text/graphics from picture blocks. Dynamic color palette that tracks recent text/graphics colors is used to separate small shape primitives of text/graphics from pictorial pixels. Shape primitives are also extracted from text/graphics blocks. All shape primitives from both block types are losslessly compressed by using a combined shape-based and palette-based coding algorithm. Then, the losslessly coded bitstream is fed into a LZW coder. Experimental results show that the SPEC has very low complexity and provides visually lossless quality while keeping competitive compression ratios.

Algorithms↗
PubMed1996

Entropy estimation of symbol sequences.

We discuss algorithms for estimating the Shannon entropy h of finite symbol sequences with long range correlations. In particular, we consider algorithms which estimate h from the code lengths produced by some compression algorithm. Our interest is in describing their convergence with sequence length, assuming no limits for the space and time complexities of the compression algorithms. A scaling law is proposed for extrapolation from finite sample lengths. This is applied to sequences of dynamical systems in non-trivial chaotic regimes, a 1-D cellular automaton, and to written English texts. (c)1996 American Institute of Physics.

Journal Article↗
PubMedDate not supplied

JPEG compression of digital echocardiographic images: impact on image quality.

Routine echocardiograms are not handled digitally because of enormous storage needs. Image compression techniques can reduce memory requirements, but the impact on diagnostic content is unknown. We studied the effect of an internationally accepted compression algorithm called Joint Photographic Experts Group (JPEG) on digital echocardiographic images. Diagnostic and image quality of JPEG compressed, two-dimensional echocardiograms at ratios ranging from 4:1 to 40:1 was compared with that of uncompressed images both subjectively by a blinded expert panel (540 observations) and objectively by quantitative image-processing parameters (180 images). Accuracy of 432 measurements performed on compressed M-mode and spectral Doppler tracings was determined. Simultaneously acquired videotaped images and their never-compressed digital counterparts were compared quantitatively for image fidelity. This study demonstrates that the JPEG compression algorithm produces little degradation of echocardiographic images at compression ratios of at least 20:1, with less measured distortion than is produced by videotape recording. With JPEG, digital storage and retrieval in routine echocardiography are feasible.

Algorithms↗
PubMed2003

Compression assessment based on medical image quality concepts using computer-generated test images.

Compression algorithms are widely used in medical imaging systems for efficient image storage, transmission, and display. In the acceptance of lossy compression algorithms in the clinical environment, important factors are the assessment of 'visually lossless' compression thresholds, as well as the development of assessment methods requiring fewer data and time than observer performance based studies. In this study a set of quantitative measurements related to medical image quality parameters is proposed for compression assessment. Measurements were carried out using region of interest (ROI) operations on computer-generated test images, with characteristics similar to radiographic images. As a paradigm, the assessment of the lossy Joint Photographic Expert Group (JPEG) algorithm, available in a telematics application for healthcare, is presented. A compression ratio of 15 was found as the visually lossless threshold for the JPEG lossy algorithm, in agreement with previous observer performance studies. Up to this ratio low contrast discrimination is not affected, image noise level is decreased, high contrast line-pair amplitude is decreased by less than 3%, and input/output gray level differences are minor (less than 1%). This type of assessment provides information regarding the type of loss, offering cost and time benefits, in parallel with the advantages of test image adaptation to the requirements of a certain imaging modality and clinical study.

Algorithms↗