Wavelet based image compression for telemedicine.
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Abstracting and Indexing↗
Biomedical subjects
P Cristea
Publications and source records attributed to P Cristea.
Wavelet medical signal processing.
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Algorithms↗
Multiresolutional distributed filtering: a novel technique that reduces the amount of data required in high resolution electrocardiography.
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Algorithms↗
Wavelet-based lossless compression of coronary angiographic images.
The final diagnosis in coronary angiography has to be performed on a large set of original images. Therefore, lossless compression schemes play a key role in medical database management and telediagnosis applications. This paper proposes a wavelet-based compression scheme that is able to operate in the lossless mode. The quantization module implements a new way of coding of the wavelet coefficients that is more effective than the classical zerotree coding. The experimental results obtained on a set of 20 angiograms show that the algorithm outperforms the embedded zerotree coder, combined with the integer wavelet transform, by 0.38 bpp, the set partitioning coder by 0.21 bpp, and the lossless JPEG coder by 0.71 bpp. The scheme is a good candidate for radiological applications such as teleradiology and picture archiving and communications systems (PACS's).
Algorithms↗
Wavelet image compression--the quadtree coding approach.
Perfect reconstruction, quality scalability, and region-of-interest coding are basic features needed for the image compression schemes used in telemedicine applications. This paper proposes a new wavelet-based embedded compression technique that efficiently exploits the intraband dependencies and uses a quadtree-based approach to encode the significance maps. The algorithm produces a losslessly compressed embedded data stream, supports quality scalability, and permits region-of-interest coding. Moreover, experimental results obtained on various images show that the proposed algorithm provides competitive lossless/lossy compression results. The proposed technique is well suited for telemedicine applications that require fast interactive handling of large image sets, over networks with limited and/or variable bandwidth.
Algorithms↗
Beat-to-beat wavelet variance of the QRS complex as a marker of arrhythmogenic substrate in ventricular tachycardia patients.
This study proposes a wavelet transform based technique to assess the beat-to-beat variation of the QRS signal in post-myocardial infarction patients with sustained monomorphic ventricular tachycardia. Recent electrophysiological investigations suggested that the diminished synchrony between the normal myocardium and the scarred arrhythmogenic tissue bordering a myocardial infarction area gives rise to beat-variable ECG signal components. Using a mathematical model of small variations in a largely repetitive waveform, we show that the inherent alignment errors (trigger jitter) of the high-resolution ECG (HRECG) can artificially increase the value of the time-domain beat-to-beat variance, making it less valuable as a marker of beat-variable signal components. To overcome this drawback, we propose the wavelet based approach which discriminates between the different factors responsible for the beat variability (the alignment error and the beat-variable signal components). The Morlet wavelet transform is performed on HRECG signals from normal individuals (control group) and postmyocardial infarction patients with documented ventricular tachycardia. Electrical variability is quantitatively assessed via the beat-to-beat wavelet variance measurements. A marker of arrhythmogenic induced variance which achieves a good performance in discrimination of ventricular tachycardia patients from normal subjects was found between 200 Hz and 300 Hz. This finding is in agreement with the proposed mathematical model which states that the useful part of the time-frequency map is shifted upward in a precise mathematical way, as the variance induced by the beat-variable arrhythmogenic signals depend on the frequency characteristics of the first derivative of these signals. We conclude that the dynamics of the arrhythmogenic substrate as revealed by the beat-to-beat wavelet variance can be a new estimator of ventricular tachycardia risk.
Algorithms↗
Cardiac electrical instability of the arrhythmogenic tissue substrate assessed by means of wavelet transform.
The unstable activation wavefront from the tissue responsible for the production of ventricular tachycardia (VT) gives rise to beat-variable signals components that are eluded during the averaging step of high resolution ECG (HRECG). We used a mathematical model of small variations in a largely repetitive waveform to evaluate the beat-to-beat variance of the HRECG signal. The ability of the Morlet Wavelet Transform to discriminate the different factors responsible for the beat-variability (the alignment error and the beat-variable signal component) has been assessed on simulated signals. The performance evaluation on real ECG signals from normal subjects and patients with a documented history of ventricular tachycardia showed that the dynamics of the arrhythmogenic substrate as revealed by wavelet transform offers a significant improvement in ventricular tachycardia risk assessment.
Computer Simulation↗