Showing papers by "Michael G. Strintzis published in 1994"

Nonlinear ultrasonic image processing based on signal-adaptive filters and self-organizing neural networks

Abstract: Two approaches for ultrasonic image processing are examined. First, signal-adaptive maximum likelihood (SAML) filters are proposed for ultrasonic speckle removal. It is shown that in the case of displayed ultrasound (US) image data the maximum likelihood (ML) estimator of the original (noiseless) signal closely resembles the L/sub 2/ mean which has been proven earlier to be the ML estimator of the original signal in US B-mode data. Thus, the design of signal-adaptive L/sub 2/ mean filters is treated for US B-mode data and displayed US image data as well. Secondly, the segmentation of ultrasonic images using self-organizing neural networks (NN) is investigated. A modification of the learning vector quantizer (L/sub 2/ LVQ) is proposed in such a way that the weight vectors of the output neurons correspond to the L/sub 2/ mean instead of the sample arithmetic mean of the input observations. The convergence in the mean and in the mean square of the proposed L/sub 2/ LVQ NN are studied. L/sub 2/ LVQ is combined with signal-adaptive filtering in order to allow preservation of image edges and details as well as maximum speckle reduction in homogeneous regions. >

Evaluation of multiresolution block matching techniques for motion and disparity estimation

Abstract: Multiresolution block matching methods for both monocular and stereoscopic image sequence coding are evaluated. These methods are seen to drastically reduce the amount of processing needed for block correspondence without seriously affecting the quality of the reconstructed images. The evaluation criteria are the prediction error and the speed of the algorithm for motion, disparity, and fused motion and disparity estimation, in comparison with the full search (exhaustive) method. A new method is also proposed based in multiresolution techniques, for efficient coding of the disparity or the displacement vector field.

Use of neural networks in detection of ischemic episodes from ECG leads

Abstract: A supervised neural network (NN) algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular the performance was measured in terms of beat-by-beat ischemia detection and in terms of ischemic episodes detection. Aggregate statistics for the description of the detector performance were used due to the small number of events. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm reduces dramatically training time (10-fold decrease in our case) when compared to the classical BP algorithm. The resulting NN is capable of detecting ischemia independently of the lead used. It was found that the average ischemia episode sensitivity is 88.62% while the average ischemia sensitivity is 72.22%. This drop in ischemia sensitivity could be attributed to the diverse statistical properties of the ECGs within the same patient. The results show that NN can be used in ECG processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCUs). >

Wavelet image compression using IIR minimum variance filters, partition priority, and multiple distribution entropy coding

Abstract: Image compression methods for progressive transmission using optimal subband/wavelet decomposition, partition priority coding (PPC) and multiple distribution entropy coding (MDEC) are presented. In the proposed coder, hierarchical wavelet decomposition of the original image is achieved using wavelets generated by IIR minimum variance filters. The smoothed subband coefficients are coded by an efficient triple state DPCM coder and the corresponding prediction error is Lloyd-Max quantized. The detail coefficients are coded using a novel hierarchical PPC (HPPC) approach. That is, given a suitable partitioning of their absolute range, the detail coefficients are ordered based on their decomposition level and magnitude, and the address map is appropriately coded. Finally, adaptive MDEC is applied to both the DPCM and HPPC outputs by considering a division of the source of the quantized coefficients into multiple subsources and adaptive arithmetic coding based on their corresponding histograms.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Design of filters for minimum-variance multiresolution analysis and subband decomposition

Abstract: The paper investigates the design of minimum-variance perfect reconstruction filter banks for use in image compression applications. These filter banks are formed so that the variance of the difference between input and output of each band is minimized. This has the effect of minimizing the error occurring when only one of all filter banks is retained and the rest are omitted. The paper presents a method for the design of such banks so as to achieve in each band minimum variance and a linear-phase realization appropriate for application to images. Some results of the actual implementation of these filter bands to image compression are included.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Joint motion/disparity estimation for stereo image sequences

Abstract: An algorithm is described for the joint estimation of motion and disparity vector fields from stereoscopic image sequences. Gibbs-Markov random fields are used to model local interaction processes. Interaction of neighboring motion, disparity vectors across a discontinuity line is prohibited via hidden Markov chains signaling discontinuities in the vector fields. The coherence of motion and disparity vector fields, is exploited by means of the epipolar constraint and the so called `loop constraint'. A simulated annealing algorithm is employed to find the global maximum of the posterior probability.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Optimal biorthogonal wavelet analysis

Abstract: The paper investigates the design of filter banks generating the optimal signal representation by M-band one-dimensional and multi-dimensional biorthogonal wavelet frames. Criterion of optimality is the minimization of the average mean-square approximation error at each level of the decomposition. Preliminary results of the application of such filter banks to two- dimensional image compression are included.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.