Brain Image Compression: A Brief Survey
---
paper_title: Enabling solutions for an efficient compression of PET-CT datasets

paper_content:

PET/CT oncological studies produce a huge amount of digital data, from several hundreds of Mbytes for static whole-body scans to the order of tens of Gbytes for gated whole-body studies. The need of efficient compression methods for these kind of image data comes up, both for archive and for transmission purpose, as, for example, for telemedicine applications, where bandwidth bottlenecks could make a data transmission service actually unusable. In this paper we propose and test an articulated coding system enabling the use of efficient compression of PET/CT multimodal datasets. In particular, we consider the opportunities coming from an exploitation of cross-modal information of PET-CT data, and we investigate methods for the reduction of computational complexity and memory usage. This lead to an increase of both reliability and coding efficiency.
---
paper_title: Locally Optimal Run-Length Compression Applied to CT Images

paper_content:

Run-length data compression techniques are described that preserve image content. After decompression, images are restored to their original state without loss in image gray scale or resolution. The first technique introduces terminology and illustrates a simple method for efficient picture archiving. It demonstrates the principle of run-length techniques. A second more general approach encodes picture information in a manner that adapts to local variation in pixel standard deviation. Among several options of compression formats, the one that delivers the best local compression is selected. Results of our compression techniques are given for several hundred computed tomography (CT) pictures with comparison to image entropy measures. A general solution to the optimal run-length compression of digital data is outlined. Routine application of the locally optimal method is also described.
---
paper_title: Application of Region Selective Embedded Zerotree Wavelet Coder in CT Image Compression

paper_content:

Compression is necessary in medical image preservation because of the huge data quantity. Medical images are different from the common images because of their own characteristics, for example, part of information in CT image is useless, and it's a kind of resource waste to save this part information. The region selective EZW coder was proposed with which only useful part of image was selected and compressed, and the test image provides good result
---
paper_title: 3-D image compression for X-ray CT images using displacement estimation

paper_content:

Summary form only given. The images used in this test are 87 slices of a head from a GE 9800 X-ray CT scanner. A slice is taken every 3 mm. Each slice is 512*512 pixels with 12 bits per pixel data. Objective measures show that the DEI coding scheme yields better compression performance than the 2-D DCT, since it takes advantage of correlation between adjacent slices along the body. >
---
paper_title: Efficient preprocessing technique for real-time lossless EEG compression

paper_content:

An efficient preprocessing technique of arranging an electroencephalogram (EEG) signal in matrix form is proposed for real-time lossless EEG compression. The compression algorithm consists of an integer lifting wavelet transform as the decorrelator with set partitioning in hierarchical trees as the source coder. Experimental results show that the preprocessed EEG signal gave improved rate-distortion performance, especially at low bit rates, and less encoding delay compared to the conventional one-dimensional compression scheme.
---
paper_title: Neural Network Based Near- Lossless Compression of EEG Signals with Non Uniform Quantization

paper_content:

Efficient compression technique is highly essential for the transmission and storage of large amount of biomedical signals. In this paper, a near- lossless scheme for the compression of EEG signals using artificial neural networks is proposed. The error (residue) signals which is obtained due to the difference between the original and the predicted EEG signals are thresolded based on a term referred as absolute error limit (AEL) such that, any error samples above the limit require more number of bits than the samples below the limit that require less number of bits. The thresholded error samples are quantized in a non-uniform manner by varying the actual bits assigned to the error samples. An arithmetic encoder is further used to improve the compression efficiency. Three adaptive neural network models, namely, single and multilayer perceptrons and Elman neural network and two classical adaptive predictors such as autoregressive model(AR) and normalized least mean-square FIR filter are used. EEG signals recorded under different physiological conditions are considered and the performance of the proposed scheme is evaluated in terms of compression ratio and the fidelity parameter, percent of root-mean-square-difference (PRD). It is found from the experimental results that the variation of error limit and quantization step decides the overall compression performance. Single- layer perceptron yields the best compression results in terms of utilizing less bit rate as well achieving low PRD values compared to other predictors.
---
paper_title: Compression of long-term EEG using power spectral density

paper_content:

We propose to use the features based on power spectral density as a descriptor of the EEG in the compression of the long-term intensive care unit EEG to obtain the temporal evolution of the recurrent patterns. Sleep EEG is used as a baseline since the sleep stages can be mapped to recurrent patterns in the background EEG. Our results indicate that the spectral features provide a better classification of the sleep EEG and assist in a better formation of homogenous clusters compared to the results obtained with the previously used features. The average overall agreement compared against manual scoring of seven sleep EEG records is 68.5%. It is an improvement compared to 62.7% obtained with the previously used features. Although our results for computer classification use only the EEG information from one frontal and one occipital channel, they are similar to the manual classification of sleep EEG, which is based on additional information.
---
paper_title: EEG compression using JPEG2000: How much loss is too much?

paper_content:

Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database. By varying compression parameters, a range of reconstructions of varying signal fidelity is produced. Although lossy compression has been applied to EEG data in previous studies, it is unclear what level of signal degradation, if any, would be acceptable to a clinician before diagnostically significant information is lost. In this paper, the reconstructed EEG signals are applied to REACT, a state-of-the-art seizure detection algorithm, in order to determine the effect of lossy compression on its seizure detection ability. By using REACT in place of a clinician, many hundreds of hours of reconstructed EEG data are efficiently analysed, thereby allowing an analysis of the amount of EEG signal distortion that can be tolerated. The corresponding compression ratios that can be achieved are also presented.
---
paper_title: Context-based lossless and near-lossless compression of EEG signals

paper_content:

We study compression techniques for electroencephalograph (EEG) signals. A variety of lossless compression techniques, including compress, gzip, bzip, shorten, and several predictive coding methods, are investigated and compared. The methods range from simple dictionary based approaches to more sophisticated context modeling techniques. It is seen that compression ratios obtained by lossless compression are limited even with sophisticated context based bias cancellation and activity based conditional coding. Though lossy compression can yield significantly higher compression ratios while potentially preserving diagnostic accuracy, it is not usually employed due to legal concerns. Hence, we investigate a near lossless compression technique that gives quantitative bounds on the errors introduced during compression. It is observed that such a technique gives significantly higher compression ratios (up to 3-bit/sample saving with less than 1% error). Compression results are reported for EEG's recorded under various clinical conditions.
---
paper_title: Multichannel Electrocardiogram Decomposition Using Periodic Component Analysis

paper_content:

In this letter, we propose the application of the generalized eigenvalue decomposition for the decomposition of multichannel electrocardiogram (ECG) recordings. The proposed method uses a modified version of a previously presented measure of periodicity and a phase-wrapping of the RR-interval, for extracting the ldquomost periodicrdquo linear mixtures of a recorded dataset. It is shown that the method is an improved extension of conventional source separation techniques, specifically customized for ECG signals. The method is therefore of special interest for the decomposition and compression of multichannel ECG, and for the removal of maternal ECG artifacts from fetal ECG recordings.
---
paper_title: An Adaptive Error Modeling Scheme for the Lossless Compression of EEG Signals

paper_content:

Lossless compression of EEG signal is of great importance for the neurological diagnosis as the specialists consider the exact reconstruction of the signal as a primary requirement. This paper discusses a lossless compression scheme for EEG signals that involves a predictor and an adaptive error modeling technique. The prediction residues are arranged based on the error count through an histogram computation. Two optimal regions are identified in the histogram plot through a heuristic search such that the bit requirement for encoding the two regions is minimum. Further improvement in the compression is achieved by removing the statistical redundancy that is present in the residue signal by using a context-based bias cancellation scheme. Three neural network predictors, namely, single-layer perceptron, multilayer perceptron, and Elman network and two linear predictors, namely, autoregressive model and finite impulse response filter are considered. Experiments are conducted using EEG signals recorded under different physiological conditions and the performances of the proposed methods are evaluated in terms of the compression ratio. It is shown that the proposed adaptive error modeling schemes yield better compression results compared to other known compression methods.
---
paper_title: Reversible compression of MR images

paper_content:

Methods for reversible coding can be classified according to the organization of the source model as either static, semi-adaptive, or adaptive. Magnetic resonance (MR) images have different statistical characteristics in the foreground and the background and separation is thus a promising path for reversible MR image compression. A new reversible compression method, based on static source models for foreground and background separately, is presented. The method is nonuniversal and uses contextual information to exploit the fact that entropy and bit rate are reduced by increasing the statistical order of the model. This paper establishes a realistic level of expectation regarding the bit rate in reversible MR image compression, in general, and the bit rate using static modeling, in particular. The experimental results show that compression using the new method can give bit rates comparable to the best existing reversible methods.
---
paper_title: Comparison of the ${^{1}{\rm H}}$ NMR Relaxation Enhancement Produced by Bacterial Magnetosomes and Synthetic Iron Oxide Nanoparticles for Potential Use as MR Molecular Probes

paper_content:

Iron oxide nanoparticle, named synthetic magnetite, as contrast agent has been widely used in clinical MRI. Recently, a new magnetite nanocrystal, called magnetosome, has been found in magnetotactic bacteria. Physicochemical and magnetorelaxo-metric characterization of bacteria magnetosomes and iron oxide nanoparticles are investigated. Bacterial magnetosomes have the larger mean aggregate size, better dispersion and obviously stronger ferromagnetism compared to synthetic magnetites. The samples of several concentrations of magnetic nanoparticles were analyzed using a clinical 3.0 T MR-scanner. The signal decay in the Magnetic Resonance images is found to change proportionally to the nanoparticles concentration. Two kinds of nanoparticles can be though as a negative contrast agent and show slight effects on T1, but strong effects on T2 weighted images. Notice that at the same concentration the signal attenuation of bacterial magnetite samples is more obvious than that of synthetic magnetite samples.
---
paper_title: 2D adaptive coil sensitivity estimation for dynamic parallel MRI reconstruction

paper_content:

Incorporation of parallel imaging technique in dynamic magnetic resonance imaging process accelerates significantly the imaging speed. The acceleration is achieved by acquiring the k-space data sparsely in each frame. To unwrap the aliased images caused by the k-space under-sampling, the reconstruction algorithms need the local sensitivity variation, the sensitivity map, of the receiving coils. Current estimation methods are unable to provide the true sensitivity map, and the calibration errors in the estimated sensitivity map can dramatically increase the image artifacts. This paper introduces a novel two-dimensional k-t modeling approach to improve the estimation of sensitivity map. A 2D model is fitted to each k-t plane and is updated for each estimated data point. The model is used to estimate the unacquired k-space data in each frame. A full k-t plane is composed using the estimated and acquired datasets and is used to obtain improved estimation of sensitivity map. Experiments on real dynamic parallel MRI data demonstrate that the proposed method significantly improve the quality of reconstructed MR images.
---
paper_title: Robust Detection and Lossless Compression of the Foreground in Magnetic Resonance Images

paper_content:

We present a collection of techniques for robust detection of the foreground (as opposed to background) in a MR volumetric image. A novel voting strategy makes our compressor more reliable. The image in which the background has been assigned a zero value is then losslessly compressed by another collection of techniques including a novel ordering of blocks to exploit an adaptive arithmetic coder. The image is segmented into few classes. Quantized data (represented by an index map and a codebook) and quantization differences are encoded separately. Correlations between slices are reduced by differential coding of the index map for consecutive slices. Correlations in the 3 dimensions are further reduced by an integer wavelet transform and by class-contextual arithmetic encoding of the quantization differences. Our compressor outperforms JPEG-LS, JPEG2000, SPIHT, and 3D-SPIHT.
---
paper_title: MR image compression by spiral coding

paper_content:

Investigates the encoding of magnetic resonance (MR) images of the human brain using various lossless techniques and presents a new form of spiral encoding. The algorithm used relies partially on the overall shape of the bounding contour of the image in achieving the compression and uses a traditional run-based technique to generate a set of tuples for a complete image. Comparisons are made between the feature-directed spiral encoding and the traditional paths; they include the scanning pattern associated with the normal raster scanned display and the path for a display that could be used in following a linearised quadtree encoding. The new method tracks the 'greater' contour of the overall image and once this path has been established and tuples recorded, then the inner contours are generated. From the one greater contour each of the inner contours, with a reducing radius towards the centre, can be created. The results for various techniques in terms of the number of bytes required to store the image are given. The new spiralling method achieves an average compression ratio of 1.49 for the set of MR images. >
---
paper_title: Comparison of the ${^{1}{\rm H}}$ NMR Relaxation Enhancement Produced by Bacterial Magnetosomes and Synthetic Iron Oxide Nanoparticles for Potential Use as MR Molecular Probes

paper_content:

Iron oxide nanoparticle, named synthetic magnetite, as contrast agent has been widely used in clinical MRI. Recently, a new magnetite nanocrystal, called magnetosome, has been found in magnetotactic bacteria. Physicochemical and magnetorelaxo-metric characterization of bacteria magnetosomes and iron oxide nanoparticles are investigated. Bacterial magnetosomes have the larger mean aggregate size, better dispersion and obviously stronger ferromagnetism compared to synthetic magnetites. The samples of several concentrations of magnetic nanoparticles were analyzed using a clinical 3.0 T MR-scanner. The signal decay in the Magnetic Resonance images is found to change proportionally to the nanoparticles concentration. Two kinds of nanoparticles can be though as a negative contrast agent and show slight effects on T1, but strong effects on T2 weighted images. Notice that at the same concentration the signal attenuation of bacterial magnetite samples is more obvious than that of synthetic magnetite samples.
---
paper_title: EEG data compression techniques

paper_content:

Electroencephalograph (EEG) and Holter EEG data compression techniques which allow perfect reconstruction of the recorded waveform from the compressed one are presented and discussed. Data compression permits one to achieve significant reduction in the space required to store signals and in transmission time. The Huffman coding technique in conjunction with derivative computation reaches high compression ratios (on average 49% on Holter and 58% on EEG signals) with low computational complexity. By exploiting this result a simple and fast encoder/decoder scheme capable of real-time performance on a PC was implemented. This simple technique is compared with other predictive transformations, vector quantization, discrete cosine transform (DCT), and repetition count compression methods. Finally, it is shown that the adoption of a collapsed Huffman tree for the encoding/decoding operations allows one to choose the maximum codeword length without significantly affecting the compression ratio. Therefore, low cost commercial microcontrollers and storage devices can be effectively used to store long Holter EEG's in a compressed format.
---
paper_title: Clinical investigation of a knowledge-based data compression algorithm for dynamic neurologic FDG-PET images

paper_content:

A knowledge-based data compression algorithm for dynamic positron emission tomography (PET) images has been developed previously (D. Feng et al., "An optimal image sampling schedule design for cerebral blood volume and partial volume correction in neurologic FDG-PET studies", The 29/sup th/ Annual Scientific Meeting of A & NZ Society of Nuclear Medicine, April 4-8, 1998, Melbourne, Australia, to appear in Australia and New Zealand Journal of Medicine), and it has been shown that this algorithm can greatly reduce image storage requirements in both spatial and temporal domains, while retaining medically relevant information. However, this algorithm has not yet been validated with real clinical dynamic image data. Here, the authors validated this algorithm in clinical dynamic brain PET studies using the [/sup 18/F] 2-fluoro-deoxy-glucose (FDG) tracer. Compression performance and image quality were evaluated. Results demonstrate that the storage requirements for dynamic PET image data can be reduced by more than 95%, without loss in diagnostic quality. Furthermore, use of this compression algorithm greatly reduces the computational complexity of further clinical image processing such as generation of functional images. It could benefit to the current expansion in medical imaging, and image data management.
---
paper_title: Whole body PET image compression

paper_content:

A major difficulty in providing rapid transfer of whole body PET (WB-PET) images to referring physicians at remote hospitals is the large size of the data set. A WB-PET study contains typically over 400 images, or over 35 Mbyte of data. Using standard high speed modems (14.4 kbps) and fast communication protocols, a transfer time of 7-8 hrs/study is required. To reduce the transfer time, a means of compressing the data is necessary. Due to the relatively high noise levels in WB-PET images, conventional error-free compression algorithms provides little or no compression. The compression can be improved by processing the image data prior to compression. In this work, different pre-processing approaches are evaluated as well as different compression algorithms. By removing all negative values, who carries little or no diagnostic information, the compression is slightly improved. A more significant improvement was achieved by masking the transaxial images such that all pixel elements located outside the body contour are set to zero. Three different masking schemes based on geometrical shapes (circular, rectangular and elliptical) were evaluated. In addition, a masking scheme based on transmission images was also investigated. Using the transmission image mask, with negative value removal, and an efficient error-free compression algorithm, compressions of up to 1:7 were obtained, with no loss in image information. >
---
paper_title: 2D adaptive coil sensitivity estimation for dynamic parallel MRI reconstruction

paper_content:

Incorporation of parallel imaging technique in dynamic magnetic resonance imaging process accelerates significantly the imaging speed. The acceleration is achieved by acquiring the k-space data sparsely in each frame. To unwrap the aliased images caused by the k-space under-sampling, the reconstruction algorithms need the local sensitivity variation, the sensitivity map, of the receiving coils. Current estimation methods are unable to provide the true sensitivity map, and the calibration errors in the estimated sensitivity map can dramatically increase the image artifacts. This paper introduces a novel two-dimensional k-t modeling approach to improve the estimation of sensitivity map. A 2D model is fitted to each k-t plane and is updated for each estimated data point. The model is used to estimate the unacquired k-space data in each frame. A full k-t plane is composed using the estimated and acquired datasets and is used to obtain improved estimation of sensitivity map. Experiments on real dynamic parallel MRI data demonstrate that the proposed method significantly improve the quality of reconstructed MR images.
---
paper_title: Robust Detection and Lossless Compression of the Foreground in Magnetic Resonance Images

paper_content:

We present a collection of techniques for robust detection of the foreground (as opposed to background) in a MR volumetric image. A novel voting strategy makes our compressor more reliable. The image in which the background has been assigned a zero value is then losslessly compressed by another collection of techniques including a novel ordering of blocks to exploit an adaptive arithmetic coder. The image is segmented into few classes. Quantized data (represented by an index map and a codebook) and quantization differences are encoded separately. Correlations between slices are reduced by differential coding of the index map for consecutive slices. Correlations in the 3 dimensions are further reduced by an integer wavelet transform and by class-contextual arithmetic encoding of the quantization differences. Our compressor outperforms JPEG-LS, JPEG2000, SPIHT, and 3D-SPIHT.
---
paper_title: Lossless compression for 3D PET

paper_content:

A new adaptive scheme is proposed for the lossless compression of positron emission tomography (PET) sinogram data. The algorithm uses an adaptive differential pulse code modulator (ADPCM) followed by a universal variable length coder (UVLC). Contrasting with Lempel-Ziv (LZ), which operates on a whole sinogram, UVLC operates very efficiently on short data blocks. This is a major advantage for real-time implementation. The algorithm is adaptive and codes data after some on-line estimations of the statistics inside each block. Its efficiency is tested when coding dynamic and static scans from two PET scanners and reaches asymptotically the entropy limit for long frames. For very short 3D frames, the new algorithm is twice more efficient than LZ. Since an application specific integrated circuit (ASIC) implementing a similar UVLC scheme is available today, a similar one should be able to sustain PET data lossless compression and decompression at a rate of 27 MBytes/sec. This algorithm is consequently a good candidate for the next generation of lossless compression engine. >
---
paper_title: Temporal compression for dynamic positron emission tomography via principal component analysis in the sinogram domain

paper_content:

In this paper, we compare various temporal analysis schemes applied to dynamic PET for improved quantification, image quality and temporal compression purposes. We compare an optimal sampling schedule (OSS) design, principal component analysis (PCA) applied in the image domain, and principal component analysis applied in the sinogram domain; for region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring reconstruction of all PCA channels. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a noise-normalized PCA in the sinogram domain gives similar compression ratio and quantitative accuracy to OSS, but with substantially better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and quantification applications.
---
paper_title: Image compression in medical image databases using set redundancy

paper_content:

Summary form only given. Image compression is achieved by eliminating various types of redundancy that exist in the pixel values. Individual gray-scale images contain interpixel, psychovisual, and coding redundancy. However, sets of similar images contain an additional type of redundancy: the set redundancy. Set redundancy is the inter-image redundancy that results from the common information found in more than one image in the set. Set redundancy can be used to improve compression. Medical imaging is one of the best application areas for the enhanced compression model (ECM) and the set redundancy compression (SRC) methods. Medical images classified by modality and type of exam are very similar to one another, because of the standard procedures used in radiology. Therefore, medical image databases contain large amounts of set redundancy, which the ECM can efficiently reduce. Tests performed on a test database of CT brain scans showed significant compression improvement when the images were pre-processed with SRC methods to reduce set redundancy. The images were obtained from a random population of patients, and the tests were performed with the standard compression techniques used in radiology: Huffman encoding, arithmetic coding, and Lempel-Ziv compression. The best improvement resulted from combining the min-max predictive method with Huffman compression. In our tests we used genetic algorithms to identify the sets of similar images in the image database.
---
paper_title: Iterative algorithms for variance reduction on compressed sinogram random coincidences in PET

paper_content:

The use of the ordinary Poisson iterative reconstruction algorithm in PET requires the estimation of expected random coincidences. In a clinical environment, random coincidences are often acquired with a delayed coincidence technique, and expected randoms are estimated through variance reduction (VR) of measured delayed coincidences. In this paper we present iterative VR algorithms for random compressed sinograms, when previously known methods are not applicable. Iterative methods have the advantage of easy adaptation to any acquisition geometry and of allowing the estimation of singles rates at the crystal level when the number of crystals is relatively small. Two types of sinogram compression are considered: axial (span) rebinning and transaxial mashing. A monotonic sequential coordinate descent algorithm, which optimizes the Least Squares objective function, is investigated. A simultaneous update algorithm, which possesses the advantage of easy parallelization, is also derived for both cases of the Least Squares and Poisson Likelihood objective function. Measured data from a Siemens TruePoint clinical scanner are used to validate the algorithm performance.
---
paper_title: Medical image compression by using three-dimensional wavelet transformation

paper_content:

This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB, In general, the smaller the slice distance, the better the 3-D compression performance.
---
paper_title: Clinical investigation of a knowledge-based data compression algorithm for dynamic neurologic FDG-PET images

paper_content:

A knowledge-based data compression algorithm for dynamic positron emission tomography (PET) images has been developed previously (D. Feng et al., "An optimal image sampling schedule design for cerebral blood volume and partial volume correction in neurologic FDG-PET studies", The 29/sup th/ Annual Scientific Meeting of A & NZ Society of Nuclear Medicine, April 4-8, 1998, Melbourne, Australia, to appear in Australia and New Zealand Journal of Medicine), and it has been shown that this algorithm can greatly reduce image storage requirements in both spatial and temporal domains, while retaining medically relevant information. However, this algorithm has not yet been validated with real clinical dynamic image data. Here, the authors validated this algorithm in clinical dynamic brain PET studies using the [/sup 18/F] 2-fluoro-deoxy-glucose (FDG) tracer. Compression performance and image quality were evaluated. Results demonstrate that the storage requirements for dynamic PET image data can be reduced by more than 95%, without loss in diagnostic quality. Furthermore, use of this compression algorithm greatly reduces the computational complexity of further clinical image processing such as generation of functional images. It could benefit to the current expansion in medical imaging, and image data management.
---
paper_title: Whole body PET image compression

paper_content:

A major difficulty in providing rapid transfer of whole body PET (WB-PET) images to referring physicians at remote hospitals is the large size of the data set. A WB-PET study contains typically over 400 images, or over 35 Mbyte of data. Using standard high speed modems (14.4 kbps) and fast communication protocols, a transfer time of 7-8 hrs/study is required. To reduce the transfer time, a means of compressing the data is necessary. Due to the relatively high noise levels in WB-PET images, conventional error-free compression algorithms provides little or no compression. The compression can be improved by processing the image data prior to compression. In this work, different pre-processing approaches are evaluated as well as different compression algorithms. By removing all negative values, who carries little or no diagnostic information, the compression is slightly improved. A more significant improvement was achieved by masking the transaxial images such that all pixel elements located outside the body contour are set to zero. Three different masking schemes based on geometrical shapes (circular, rectangular and elliptical) were evaluated. In addition, a masking scheme based on transmission images was also investigated. Using the transmission image mask, with negative value removal, and an efficient error-free compression algorithm, compressions of up to 1:7 were obtained, with no loss in image information. >
---
paper_title: Application of Region Selective Embedded Zerotree Wavelet Coder in CT Image Compression

paper_content:

Compression is necessary in medical image preservation because of the huge data quantity. Medical images are different from the common images because of their own characteristics, for example, part of information in CT image is useless, and it's a kind of resource waste to save this part information. The region selective EZW coder was proposed with which only useful part of image was selected and compressed, and the test image provides good result
---
paper_title: A new computation compression scheme based on a multifrequential approach

paper_content:

Nowadays medical applications such as functional imaging (PET and SPECT) or interventional imaging (CT fluoroscopy) can involve dynamic data. Thus, the computation time must be reduced. Therefore, the authors developed a new fast algorithm for dynamic reconstruction based on computation compression. The computation compression and data compression have the same fundamental steps of "decomposition", "quantification" and "uncompression". The authors' algorithm performs an indirect subband decomposition of f(f=/spl Sigma/f/sub i/) through Rf which is filtered. But as for data compression, they want to preserve the pertinent information only. To obtain this result, they reduce the number of components: Rf/sub i/ is filtered and backprojected only if f/sub i/ is significant (quantification step). Finally, to estimate signal, the authors uncompress the components (uncompression step). They implement their approach with a subband Fourier decomposition. But alternatively Cosinus, Wavelet or Karhunen Loeve decomposition could also be used. This algorithm allows to control the compression ratio and the signal quality. It yields an easy parallelization and leads to a computation time reduction directly related to the data compression rate.
---
paper_title: Comparative Analysis of Image Compression Techniques: A Case Study on Medical Images

paper_content:

Ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) medical imaging produce human body pictures in digital form. These medical applications have already been integrated into mobile devices and are being used by medical personnel in treatment centers, for retrieving and examining patient data and medical images. Storage and transmission are key issues in such platforms, due to the significant image file sizes. Wavelet transform has been considered to be a highly efficient technique of image compression resulting in both lossless and lossy compression of images with great accuracy, enabling its use on medical images. On the other hand, in some areas in medicine, it may be sufficient to maintain high image quality only in the region of interest i.e. in diagnostically important regions. This paper proposes a framework for ROI based compression of medical images using wavelet based compression techniques (i.e. JPEG2000 and SPIHT). Results are analyzed by conducting the experiments on a number of medical images by taking different region of interests. The performance is evaluated using various image quality metrics like PSNR, SSIM and Correlation.
---
paper_title: Lossless compression for 3D PET

paper_content:

A new adaptive scheme is proposed for the lossless compression of positron emission tomography (PET) sinogram data. The algorithm uses an adaptive differential pulse code modulator (ADPCM) followed by a universal variable length coder (UVLC). Contrasting with Lempel-Ziv (LZ), which operates on a whole sinogram, UVLC operates very efficiently on short data blocks. This is a major advantage for real-time implementation. The algorithm is adaptive and codes data after some on-line estimations of the statistics inside each block. Its efficiency is tested when coding dynamic and static scans from two PET scanners and reaches asymptotically the entropy limit for long frames. For very short 3D frames, the new algorithm is twice more efficient than LZ. Since an application specific integrated circuit (ASIC) implementing a similar UVLC scheme is available today, a similar one should be able to sustain PET data lossless compression and decompression at a rate of 27 MBytes/sec. This algorithm is consequently a good candidate for the next generation of lossless compression engine. >
---
paper_title: Temporal compression for dynamic positron emission tomography via principal component analysis in the sinogram domain

paper_content:

In this paper, we compare various temporal analysis schemes applied to dynamic PET for improved quantification, image quality and temporal compression purposes. We compare an optimal sampling schedule (OSS) design, principal component analysis (PCA) applied in the image domain, and principal component analysis applied in the sinogram domain; for region-of-interest quantification, sinogram-domain PCA is combined with the Huesman algorithm to quantify from the sinograms directly without requiring reconstruction of all PCA channels. Using a simulated phantom FDG brain study and three clinical studies, we evaluate the fidelity of the compressed data for estimation of local cerebral metabolic rate of glucose by a four-compartment model. Our results show that using a noise-normalized PCA in the sinogram domain gives similar compression ratio and quantitative accuracy to OSS, but with substantially better precision. These results indicate that sinogram-domain PCA for dynamic PET can be a useful preprocessing stage for PET compression and quantification applications.
---

<format>
Title: Brain Image Compression: A Brief Survey

Section 1: INTRODUCTION
Description 1: Write an introduction about the significance of digital image processing, particularly in a medical context, and explain the need for improved techniques in brain image compression.

Section 2: BRAIN IMAGE COMPRESSION
Description 2: Discuss the concept of image compression and its importance in medical imaging. Provide an overview of existing work in this area.

Section 3: TECHNIQUES OF IMAGE COMPRESSION
Description 3: Describe the various types of image compression techniques, including lossless compression methods, and detail subtypes such as run-length encoding, Huffman encoding, LZW coding, vector quantization, fractal coding, block truncation coding, and sub-band coding.

Section 4: TECHNIQUES OF BRAIN IMAGE COMPRESSION
Description 4: Provide a detailed analysis of the different compression techniques specifically applied to brain images, categorized by image type such as CT, EEG, MRI, and PET. Include summaries and discussions of specific methods and their results.

Section 5: DISCUSSION
Description 5: Summarize the analysis of the various approaches and methods reviewed in the study. Discuss the properties and challenges of brain image compression, stressing the importance of image quality, scalability, region of interest coding, meta-information, and processing power.

Section 6: CONCLUSION
Description 6: Conclude the study by summarizing the findings about brain image compression techniques, highlighting the need for further research and development in this field.
</format>
