Using phase space reconstruction for patient independent heartbeat classification in comparison with some benchmark methods.

Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively few of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, using phase space reconstruction in order to classify five heartbeat types can fill this gap to some extent. In the first and second method, Reconstructed phase space (RPS) is modeled by the Gaussian mixture model (GMM) and bins, respectively, and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before, for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% classification accuracy.

A template matching algorithm for sperm tracking and classification.

The conventional assessment of human semen, especially sperm movement characteristics, is a highly subjective assessment, with considerable intra- and inter-technician variability. Computer-assisted sperm analysis (CASA) systems provide a rapid and automated assessment of the parameters of sperm motion, together with improved standardization and quality control. However, it should be noted that the measurement of the sperm head motion by CASA is sensitive to the technique of experimentation. While conventional CASA systems use digital microscopes with phase-contrast accessories that make the sperm's head appear brighter and sharper than the other parts, in this research, a regular light microscope was used with a digital camera directly attached to its eyepiece. One of the drawbacks of this method is that the images lack proper contrast and sharpness. To remedy this, we have proposed an algorithm for sperm tracking that is insensitive to image acquisition conditions. This tracking algorithm was used after the background and extra particles were successfully removed through a two-step enhancement algorithm. Additionally, in this research, a template matching method was used for finding the sperm's path. Upon examination, it was proven that our tracking algorithm worked well with different image acquisition conditions. This paper explains how this method reduces error probability in finding and tracking sperm in various frames.

Novel genetic-neuro-fuzzy filter for speckle reduction from sonography images.

Edge-preserving speckle noise reduction is essential to computer-aided ultrasound image processing and understanding. A new class of genetic-neuro-fuzzy filter is proposed to optimize the trade-off between speckle noise removal and edge preservation. The proposed approach combines the advantages of the fuzzy, neural, and genetic paradigms. Neuro-fuzzy approaches are very promising for nonlinear filtering of noisy images. Fuzzy reasoning embedded into the network structure aims at reducing errors while fine details are being processed. The learning method based on the real-time genetic algorithms (GAs) performs an effective training of the network from a collection of training data and yields satisfactory results after a few generations. The performance of the proposed filter has been compared with that of the commonly used median and Wiener filters in reducing speckle noises on ultrasound images. We evaluate this filter by passing the filter's output to the edge detection algorithm and observing its ability to detect edge pixels.Experimental results show that the proposed genetic-neuro-fuzzy technique is very effective in speckle noise reduction as well as detail preserving even in the presence of highly noise corrupted data, and it works significantly better than other well-known conventional methods in the literature.