Tomasz Markiewicz | PubMed Health

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↗