RESUMO
In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the "center-surround" structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.
Assuntos
Redes Neurais de Computação , Neurônios , Algoritmos , HumanosRESUMO
Motion analysis or quality assessment of human sperm cell is great important for clinical applications of male infertility. Sperm tracking is quite complex due to cell collision, occlusion and missed detection. The aim of this study is simultaneous tracking of multiple human sperm cells. In the first step in this research, the frame difference algorithm is used for background subtraction. There are some limitations to select an appropriate threshold value since the output accuracy is strongly dependent on the selected threshold value. To eliminate this dependency, we propose an improved non-linear diffusion filtering in the time domain. Non-linear diffusion filtering is a smoothing and noise removing approach that can preserve edges in images. Many sperms that move with different speeds in different directions eventually coincide. For multiple tracking over time, an optimal matching strategy is introduced that is based on the optimization of a new cost function. A Hungarian search method is utilized to obtain the best matching for all possible candidates. The results show nearly 3.24% frame based error in dataset of videos that contain more than 1 and less than 10 sperm cells. Hence the accuracy rate was 96.76%. These results indicate the validity of the proposed algorithm to perform multiple sperms tracking.
RESUMO
Vessel extraction is a critical task in clinical practice. In this paper, we propose a new approach for vessel extraction using an active contour model by defining a novel vesselness-based term, based on accurate analysis of the vessel structure in the image. To achieve the novel term, a simple and fast directional filter bank is proposed, which does not employ down sampling and resampling used in earlier versions of directional filter banks. The proposed model not only preserves the performance of the existing models on images with intensity inhomogeneity, but also overcomes their inability both to segment low contrast vessels and to omit non-vessel structures. Experimental results for synthetic images and coronary X-ray angiograms show desirable performance of our model.
