Biomedical subjects

M Strintzis

Publications and source records attributed to M Strintzis.

4 recordsLinked to original sources

PubMed1997

A new automated method for three-dimensional registration of MR images of the head.

A new technique for three-dimensional image registration was developed and tested using T1 and T2 weighted Magnetic Resonance image studies of the head. The method uses the fuzzy c-means classification algorithm for outlining the surface contours and then minimizes iteratively the mean squared value of the voxel per voxel weighted ratio of the two trilinearly interpolated cubic voxel volumes. A total of 200 two-dimensional and 240 three-dimensional registration experiments were performed and showed that the method is signal intensity independent, it has registration accuracy better than 1 degree for rotations and 1 voxel for translations and it is not affected by the deterioration in the imaging resolution for voxel sizes up to 1.8 mms.

Brain Diseases↗

PubMedDate not supplied

A fast and accurate method for registration of MR images of the head.

This paper proposes a new fully automated technique that can be used for the registration of medical images of the head. The method uses Chebyshev polynomials in order to approximate and then minimize a novel multiresolutional, signal intensity independent disparity function, which can generally be defined as the mean squared value of the mean weighted ratio of two images. This function is explicitly computed for n Chebyshev points in a geometric transformation parameter interval [-A, +A] transformation units and is approximated using the Chebyshev polynomials for all other points in the interval. For 3D T2-T1 weighted MR registration, 120 experiments with studies from ten patients were performed and showed that n = 4 Chebyshev points for A = 18 transformation units give mean rotational error 0.36 degrees and a mean translational error 0.36 mm. The different noise conditions did not affect the performance of the method. We conclude that the method is suitable for routine clinical applications and that it has significant potential for future development and improvement.

Algorithms↗

PubMedDate not supplied

ECG pattern recognition and classification using non-linear transformations and neural networks: a review.

The most widely used signal in clinical practice is the ECG. ECG conveys information regarding the electrical function of the heart, by altering the shape of its constituent waves, namely the P, QRS, and T waves. Thus, the required tasks of ECG processing are the reliable recognition of these waves, and the accurate measurement of clinically important parameters measured from the temporal distribution of the ECG constituent waves. In this paper, we shall review some current trends on ECG pattern recognition. In particular, we shall review non-linear transformations of the ECG, the use of principal component analysis (linear and non-linear), ways to map the transformed data into n-dimensional spaces, and the use of neural networks (NN) based techniques for ECG pattern recognition and classification. The problems we shall deal with are the QRS/PVC recognition and classification, the recognition of ischemic beats and episodes, and the detection of atrial fibrillation. Finally, a generalised approach to the classification problems in n-dimensional spaces will be presented using among others NN, radial basis function networks (RBFN) and non-linear principal component analysis (NLPCA) techniques. The performance measures of the sensitivity and specificity of these algorithms will also be presented using as training and testing data sets from the MIT-BIH and the European ST-T databases.

Algorithms↗

PubMedDate not supplied

ECG processing techniques based on neural networks and bidirectional associative memories.

Two ECG processing techniques are described for the classification of QRSs, PVCs and normal and ischaemic beats. The techniques use neural network (NN) technology in two ways. The first technique, uses nonlinear ECG mapping preprocessing and subsequently for classification uses a shrinking algorithm based on NNs. This technique is applied to the QRS/PVC problem with good result. The second technique is based on the Bidirectional Associative Memory (BAM) NN and is used to distinguish normal from ischaemic beats. In this technique the ECG beat is treated as a digitized image which is then transformed into a bipolar vector suitable for input in the BAM. The results show that this method, if properly calibrated, can result in a fast and reliable ischaemic beat detection algorithm.

Algorithms↗