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PubMed2005

Performance evaluation of functional medical imaging compression via optimal sampling schedule designs and cluster analysis.

In previous work we have described a technique for the compression of positron emission tomography (PET) image data in the spatial and temporal domains based on optimal sampling schedule designs (OSS) and cluster analysis. It can potentially achieve a high data compression ratio greater than 80:1. However, the number of distinguishable cluster groups in dynamic PET image data is a critical issue for this algorithm that has not been experimentally analyzed on clinical data. In this paper, the problem of experimentally determining the ideal cluster number for the algorithm for PET brain data is addressed.

Algorithms↗
PubMed2005

DMLC leaf-pair optimal control for mobile, deforming target.

Existing algorithms of dynamic control of independent pairs of leaves allow optimal DMLC delivery of IMRT to rigid targets translating parallel to leaf trajectories. However, in numerous cases of radiotherapy treatments simplifying assumptions of rigid-like motions of targets and surrounding tissues are clearly not satisfied. Therefore algorithms have to be developed that allow one to control MLC so that predetermined intensities are delivered to various points in targets that experience compression and expansion at the time of irradiation. Moreover, it is desirable for such algorithms to ensure that delivery of modulated intensity map will be done with minimal expense of monitor units. Derivation of the algorithm that optimizes the DMLC IMRT to mobile, deforming target is presented in this paper. [To illustrate the general algorithm two representative examples of DMLC IMRT delivery to deforming targets are presented in full detail.] Finally, similarities and differences between solutions for immobile targets, for moving, rigid targets and for moving, deforming targets are discussed.

Algorithms↗
PubMed2006

Stochastic automata-based estimators for adaptively compressing files with nonstationary distributions.

This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to data compression applications in which the data distributions are nonstationary. The adaptive coding scheme utilizes stochastic learning-based weak estimation techniques to adaptively update the probabilities of the source symbols, and this is done without resorting to either maximum likelihood, Bayesian, or sliding-window methods. The authors have incorporated the estimator in the adaptive Fano coding scheme and in an adaptive entropy-based scheme that "resembles" the well-known arithmetic coding. The empirical results obtained for both of these adaptive methods are obtained on real-life files that possess a fair degree of nonstationarity. From these results, it can be seen that the proposed schemes compress nearly 10% more than their respective adaptive methods that use maximum-likelihood estimator-based estimates.

Algorithms↗
PubMed2004

Maximum likelihood principal components regression on wavelet-compressed data.

Maximum likelihood principal component regression (MLPCR) is an errors-in-variables method used to accommodate measurement error information when building multivariate calibration models. A hindrance of MLPCR has been the substantial demand on computational resources sometimes made by the algorithm, especially for certain types of error structures. Operations on these large matrices are memory intensive and time consuming, especially when techniques such as cross-validation are used. This work describes the use of wavelet transforms (WT) as a data compression method for MLPCR. It is shown that the error covariance matrix in the wavelet and spectral domains are related through a two-dimensional WT. This allows the user to account for any effects of the wavelet transform on spectral and error structures. The wavelet transform can be applied to MLPCR when using either the full error covariance matrix or the smaller pooled error covariance matrix. Simulated and experimental near-infrared data sets are used to demonstrate the benefits of using wavelets with the MLPCR algorithm. In all cases, significant compression can be obtained while maintaining favorable predictive ability. Considerable time savings were also attained, with improvements ranging from a factor of 2 to a factor of 720. Using the WT-compressed data in MLPCR gave a reduction in prediction errors compared to using the raw data in MLPCR. An analogous reduction in prediction errors was not always seen when using PCR.

Journal Article↗
PubMed2004

A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables.

An algorithm is proposed for extracting relevant information from near-infrared (NIR) spectra for multivariate calibration of routine components in complex plant samples. The algorithm is a combination of wavelet transform (WT) data compression and a procedure for uninformative variable elimination (UVE). After compression of the NIR spectra by WT, the UVE approach is used to eliminate the irrelevant wavelet coefficients. Finally, a calibration model is built from the retained wavelet coefficients to enable prediction. Because irrelevant information can be removed from the spectra used for multivariate calibration, the model based on the extracted relevant features is better than those obtained with full-spectrum data. Both prediction precision and calculation speed are improved.

Journal Article↗
PubMed2000

Dynamics of errors in 3D motion estimation and implications for strain-tensor imaging in acoustic elastography.

For the purpose of quantifying the noise in acoustic elastography, a displacement covariance matrix is derived analytically for the cross-correlation based 3D motion estimator. Static deformation induced in tissue from an external mechanical source is represented by a second-order strain tensor. A generalized 3D model is introduced for the ultrasonic echo signals. The components of the covariance matrix are related to the variances of the displacement errors and the errors made in estimating the elements of the strain tensor. The results are combined to investigate the dependences of these errors on the experimental and signal-processing parameters as well as to determine the effects of one strain component on the estimation of the other. The expressions are evaluated for special cases of axial strain estimation in the presence of axial, axial-shear and lateral-shear type deformations in 2D. The signals are shown to decorrelate with any of these deformations, with strengths depending on the reorganization and interaction of tissue scatterers with the ultrasonic point spread function following the deformation. Conditions that favour the improvements in motion estimation performance are discussed, and advantages gained by signal companding and pulse compression are illustrated.

Acoustics↗
PubMed1999

Multiresolution analysis of emission tomography images in the wavelet domain.

This article develops a theoretical framework for the use of the wavelet transform in the estimation of emission tomography images. The solution of the problem of estimation addresses the equivalent problems of optimal filtering, maximum compression, and statistical testing. In particular, new theory and algorithms are presented that allow current wavelet methodology to deal with the two main characteristics of nuclear medicine images: low signal-to-noise ratios and correlated noise. The technique is applied to synthetic images, phantom studies, and clinical images. Results show the ability of wavelets to model images and to estimate the signal generated by cameras of different resolutions in a wide variety of noise conditions. Moreover, the same methodology can be used for the multiscale analysis of statistical maps. The relationship of the wavelet approach to current hypothesis-testing methods is shown with an example and discussed. The wavelet transform is shown to be a valuable tool for the numerical treatment of images in nuclear medicine. It is envisaged that the methods described here may be a starting point for further developments in image reconstruction and image processing.

Algorithms↗
PubMed2005

Characterization of force chains in granular material.

It has been observed that the majority of particles in a granular material carries less than the average load and that the number of particles carrying larger than the average load decreases exponentially with increasing contact force. The particles carrying above average load appear to form a strong network of forces while the majority of particles belong to a weak network. The strong network of forces appear to have a spatial characteristic whereby the stronger forces are carried though chainlike particle groups referred to as force chains. There is a strong case for a connection between force chains of the discrete medium and the trajectory of the most compressive principal stress in its continuous idealization. While such properties seem obvious from descriptive analysis of physical and numerical experiments in granular media, progress in quantification of the force chain statistics requires an objective description of what constitutes a force chain. A procedure to quantify the occurrence of force chains is built on a proposed definition having two parts: first, the chain is a quasilinear arrangement of three or more particles, and second, along the chain, stress concentration within each grain is characterized by the vector delineating the most compressive principal stress. The procedure is incorporated into an algorithm that can be applied to large particle assemblies to compile force chain statistics. The procedure is demonstrated on a discrete element simulation of a rigid punch into a half space. It was found that only approximately half of the particles within the group of so-called strong network particles are part of force chains. Throughout deformation, the average length of force chains varied slightly but the number of force chains decreased as the punch advanced. The force chain lengths follow an exponential distribution. The procedure provides a tool for objective analysis of force chains, although future work is required to incorporate branching of force chains into the analysis.

Journal Article↗
PubMed1997

EEG data compression techniques.

In this paper, 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.

Algorithms↗
PubMed2006

Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images.

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.

Algorithms↗
PubMed1994

Some experiments in compressing angiocardiographic images according to the Peano-Hilbert scan path.

We defined and implemented three new irreversible compression techniques for digital angiocardiographic static images: brightness error limitation (BEL), pseudo-gradient adaptive brightness error limitation (PABEL), pseudo-gradient adaptive brightness and contrast error limitation (PABCEL). To scan digital images we implemented an algorithm based on the Peano-Hilbert plane filling curve. We applied our compression techniques to 168 static images selected from angiocardiographic 35-mm films. We achieved best compression results applying the PABCEL method, obtaining a mean compression ratio of about 8:1. Consulted cardiologists did not find significant diagnostic differences between original images and reconstructed ones.

Algorithms↗
PubMed2003

Compensation of log-compressed images for 3-D ultrasound.

In this study, a Bayesian approach was used for 3-D reconstruction in the presence of multiplicative noise and nonlinear compression of the ultrasound (US) data. Ultrasound images are often considered as being corrupted by multiplicative noise (speckle). Several statistical models have been developed to represent the US data. However, commercial US equipment performs a nonlinear image compression that reduces the dynamic range of the US signal for visualization purposes. This operation changes the distribution of the image pixels, preventing a straightforward application of the models. In this paper, the nonlinear compression is explicitly modeled and considered in the reconstruction process, where the speckle noise present in the radio frequency (RF) US data is modeled with a Rayleigh distribution. The results obtained by considering the compression of the US data are then compared with those obtained assuming no compression. It is shown that the estimation performed using the nonlinear log-compression model leads to better results than those obtained with the Rayleigh reconstruction method. The proposed algorithm is tested with synthetic and real data and the results are discussed. The results have shown an improvement in the reconstruction results when the compression operation is included in the image formation model, leading to sharper images with enhanced anatomical details.

Algorithms↗
PubMed2006

Reversing demosaicking and compression in color filter array image processing: performance analysis and modeling.

In the conventional processing chain of single-sensor digital still cameras (DSCs), the images are captured with color filter arrays (CFAs) and the CFA samples are demosaicked into a full color image before compression. To avoid additional data redundancy created by the demosaicking process, an alternative processing chain has been proposed to move the compression process before the demosaicking. Recent empirical studies have shown that the alternative chain can outperform the conventional one in terms of image quality at low compression ratios. To provide a theoretically sound basis for such conclusion, we propose analytical models for the reconstruction errors of the two processing chains. The models developed confirm the results of existing empirical studies and provide better understanding of DSC processing chains. The modeling also allows performance predictions for more advanced compression and demosaicking methods, thus providing important cues for future development in this area.

Algorithms↗
PubMed2005

On multirate optimality of JPEG2000 code stream.

Arguably, the most important and defining feature of the JPEG2000 image compression standard is its R-D optimized code stream of multiple progressive layers. This code stream is an interleaving of many scalable code streams of different sample blocks. In this paper, we reexamine the R-D optimality of JPEG2000 scalable code streams under an expected multirate distortion measure (EMRD), which is defined to be the average distortion weighted by a probability distribution of operational rates in a given range, rather than for one or few fixed rates. We prove that the JPEG2000 code stream constructed by embedded block coding of optimal truncation is almost optimal in the EMRD sense for uniform rate distribution function, even if the individual scalable code streams have nonconvex operational R-D curves. We also develop algorithms to optimize the JPEG2000 code stream for exponential and Laplacian rate distribution functions while maintaining compatibility with the JPEG2000 standard. Both of our analytical and experimental results lend strong support to JPEG2000 as a near-optimal scalable image codec in a fairly general setting.

Algorithms↗
PubMed2003

An approach to improve the offshore platform coordinates accuracy by using multichannel Kalman filtering.

In this paper, multichannel Kalman filters for estimation of offshore platform (OP) coordinates are designed. The complete OP motion is assumed to be composed of the low-frequency motion caused by the wind and undercurrent, and the high-frequency motion caused by the sea. The mathematical model of the low-frequency OP motion is given by the normal differential equation system, and the high-frequency OP motion is represented by a moving-average multivariable autoregression model. The parameter estimation problem for the model of the low-frequency OP motion, on which the in-service control is performed, is solved through two jointly operating Kalman filters: the first one is for the estimation of the parameters of the low-frequency motion model, and the second one is for the parameter estimation for the high-frequency model. The parameters of the first filter are automatically adapted to variations of the second filter, i.e., they are adapted to disturbances from the sea. Two algorithms for the OP motion parameter estimation (parallel and with preliminary data compression). employed for several measuring channels data estimation, are developed, and simulated on a computer. Some recommendations on their use are given.

Algorithms↗
PubMed2005

Optimal erasure protection strategy for scalably compressed data with tree-structured dependencies.

This paper is concerned with the transmission of scalably compressed data sources over lossy channels. Specifically, this paper is concerned with packet networks or, more generally, erasure channels. Previous work has generally assumed that the source elements form linear dependencies. The contribution of this paper is an unequal erasure protection algorithm which is able to take advantage of scalable data with more general dependency structures. In particular, the proposed scheme is adapted to data with tree-structured dependencies. The source elements are allocated to clusters of packets according to their dependency structure, subject to constraints on packet size and channel code-word length. Given a packet cluster arrangement, source elements are assigned optimal channel codes subject to a constraint on the total transmission length. Experimental results confirm the benefit associated with exploiting the actual dependency structure of the data.

Algorithms↗
PubMed2000

A finite element method for mechanical response of hair cell ciliary bundles.

This paper describes the development of a methodology for performing a mechanical analysis of hair cell ciliary bundles. The cilia were modeled as shear deformable beams, and interconnections were modeled as two-force members. These models were incorporated into software, which performs a finite element analysis of a user-defined bundle. The algorithm incorporates aspects of the bundle such as geometric realignment and buckling of compressed side links. A sample bundle is introduced and results of modeling it are presented.

Actins↗
PubMed2002

Ultrasonic elastography using sector scan imaging and a radial compression.

Elastography is an imaging technique based on strain estimation in soft tissues under quasi-static compression. The stress is usually created by a compression plate, and the target is imaged by an ultrasonic linear array. This configuration is used for breast elastography, and has been investigated both theoretically and experimentally. Phenomena such as strain decay with tissue depth and strain concentrations have been reported. However in some in vivo situations, like prostate or blood vessels imaging, this set-up cannot be used. We propose a device to acquire in vivo elastograms of the prostate. The compression is applied by inflating a balloon that covers a transrectal sector probe. The 1D algorithm used to calculate the radial strain fails if the center of the imaging probe does not correspond to the center of the compressor. Therefore, experimental elastograms are calculated with a 2D algorithm that accounts for tangential displacements of the tissue. In this article, in order to gain a better understanding of the image formation process, the use of ultrasonic sector scans to image the radial compression of a target is investigated. Elastograms of homogeneous phantoms are presented, and compared with simulated images. Both show a strain decay with tissue depth. Then experimental and simulated elastograms of a phantom that contains a hard inclusion are presented, showing that strain concentrations occur as well. A method to compensate for strain decay and therefore to increase the contrast of the strain elastograms is proposed. It is expected that such information will help to interpret and possibly improve the elastograms obtained via radial compression.

Algorithms↗