Biomedical subjects

Stanislaw Osowski

Publications and source records attributed to Stanislaw Osowski.

4 recordsLinked to original sources

PubMed2006

Image processing for accurate cell recognition and count on histologic slides.

OBJECTIVE: To design an automatic system for recognition and count of two different cell families on histologic slides. STUDY DESIGN: The segmentation strategy uses color information on the image. The morphologic operations and Support Vector Machine approaches are used for each color to obtain precise segmentation of the image into separate cells for recognition. RESULTS: A large set of histologic slides of bone marrow was assessed byour system and the results compared to the score of a human expert. The results are in good agreement. The difference is within acceptable limits (below 10%). CONCLUSION: The automatic system of cell recognition and extraction is accurate and provides a useful tool for cell recognition and count on histologic slides.

Cell Count↗

PubMed2004

Support vector machine-based expert system for reliable heartbeat recognition.

This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.

Algorithms↗

PubMed2002

Higher order statistics and neural network for tremor recognition.

This paper is concerned with the tremor characterization for the purpose of recognition. Three different types of tremor are considered in this paper: the parkinsonian, essential, and physiological. It has been proven that standard second-order statistical description of tremor is not sufficient to distinguish between these three types. Higher order polyspectra based on third- and fourth-order cumulants have been proposed as the additional characterization of the tremor time series. The set of 30 quantities based on the polyspectra has been proposed and investigated as the features for the recognition of tremor. The neural network of the multilayer perceptron structure has been used as a classifier. The results of numerical experiments have proven high efficiency of the proposed approach. The average error of recognition of three types of tremor did not exceed 3%.

Computer Simulation↗

PubMed1996

Fast Second Order Learning Algorithm for Feedforward Multilayer Neural Networks and its Applications.

The paper presents the efficient training program of multilayer feedforward neural networks. It is based on the best second order optimization algorithms including variable metric and conjugate gradient as well as application of directional minimization in each step. Its efficiency is proved on the standard tests, including parity, dichotomy, logistic and two-spiral problems. The application of the algorithm to the solution of higher dimensionality problems like deconvolution, separation of sources and identification of nonlinear dynamic plant are also given and discussed. It is shown that the appropriately trained neural network can be used for the nonconventional solution of these standard signal processing tasks with satisfactory accuracy. The results of numerical experiments are included and discussed in the paper. Copyright 1996 Elsevier Science Ltd.

Journal Article↗