Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neu...
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Stochastic resonance (SR) has been shown to enhance the signal to noise ratio or detection of signals in neurons. It is not yet clear how this effect of SR on the signal to noise ratio affects signal processing in neural networks. In this paper, we test the hypothesis that SR can improve information transmission in the hippocampus. From spike firing times recorded at the soma, the inter spike intervals were generated and then "total" and "noise" entropies were estimated to obtain the mutual information and information rate of the spike trains. The results show that the information rate reached a maximum value at a specific amplitude of the background noise, implying that the stochastic resonance can improve the information transmission in the CA1 neuron model. Furthermore, the results also show that the effect of stochastic resonance tended to decrease as the intensity of the random sub-threshold spike trains (signal) (more than 20 l/s) approached to that of the background noise (100 l/s). In conclusion, the computation results that the stochastic resonance can improve information processing in the hippocampal CA1 neuron model in which the intensity of the random sub-threshold spike trains was set at 5-20 l/s
This paper analyzes the effect of custom error control schemes on the energy efficiency in Bluetooth sensor networks. The energy efficiency metric considers in just one parameter the energy and reliability constraints...
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This paper analyzes the effect of custom error control schemes on the energy efficiency in Bluetooth sensor networks. The energy efficiency metric considers in just one parameter the energy and reliability constraints of the wireless sensor networks. New packet types are introduced using some error control strategies in the AUX1 packet, such as Hamming and BCH codes, with and without CRC for error detection. Two adaptive techniques are proposed that change the error control strategy based on the number of hops traversed by a packet through the network. The performance results are obtained through simulations in a channel with Rayleigh fading for networks with different number of hops, showing that error control can improve the energy efficiency of a Bluetooth-based sensor network.
Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharm...
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Atherosclerosis at the carotid bifurcation resulting in cerebral emboli is a major cause of ischemic stroke. Most strokes associated with carotid atherosclerosis can be prevented by lifestyle/dietary changes and pharmacological treatments if identified early by monitoring carotid plaque changes. Sensitive monitoring of plaque changes in volume and morphology requires that 3D ultrasound (US) images of carotid plaque obtained at different time points be registered and evaluated for change. This registration technique should be non-rigid, since different head positions in image acquisitions cause relative bending and torsion in the neck, producing non-linear deformations between the images. We modeled the movement of the neck using a "twisting and bending model" with only six parameters for non-rigid registration. We used a Mutual Information (MI) based image similarity measure with the Powell optimization method as they have been used effectively in US image registration applications. For evaluation of our algorithm, we acquired 3D US carotid images from three subjects at two different head positions to simulate images acquired at different times. Then, we registered each image set using our "twisting bending model" based non-rigid registration algorithm. We calculated the Mean Registration Error (MRE) between the segmented vessel surfaces in the target image and the registered image using a distance-based error metric. We repeated the experiment with only rigid registration to compare the capabilities of the proposed algorithm in improving registration of 3D carotid US images. The average registration error was 1.03plusmn0.23 mm using our non-rigid registration technique, while it was 1.50plusmn0.50 mm when we applied the rigid registration alone
This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. ...
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This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.
The progressive image transmission (PIT) technique has been used to alleviate the communication problem related to transmit large volume of medical image data. In this study, we propose a novel PIT algorithm based on ...
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The progressive image transmission (PIT) technique has been used to alleviate the communication problem related to transmit large volume of medical image data. In this study, we propose a novel PIT algorithm based on wavelet transform, DPCM coding and non-uniform scalar quantization. Experimental results have confirmed the efficiency of the proposed scheme. The achieved bit rate for the first recognizable picture can be as low as 0.05 bit/pixel transmitted in less than 1.0 second for a 512times512 256-gray scale medical image. The reconstructed image shows higher quality than that obtained by the set partitioning in hierarchical trees (SPIHT) algorithm, which makes it a winning choice for medical image transmission through low speed communication channels
We demonstrate, for the first time, micromechanical detection of the neurotransmitter dopamine and its discrimination from ascorbic acid. Microcantilever sensors were fabricated and coated with the polysaccharide chit...
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The analysis of jaw movement has long been used as a measure for clinical diagnosis and treatment of prosthodontics, orthodontics, and oral surgery. However, prior analysis methods focus on the trajectory of the mandi...
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In this paper, we propose a knowledge-driven highly automatic methodology for extracting the masseter from magnetic resonance (MR) data sets for clinical purposes. The masseter is a muscle of mastication which acts to...
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In this paper, we propose a knowledge-driven highly automatic methodology for extracting the masseter from magnetic resonance (MR) data sets for clinical purposes. The masseter is a muscle of mastication which acts to raise the jaw and clench the teeth. In our initial work, we designed a process which allowed us to perform 2-D segmentation of the masseter on 2-D MR images. In the methodology proposed here, we make use of ground truth to first determine the index of the MR slice in which we will carry out 2-D segmentation of the masseter. Having obtained the 2-D segmentation, we will make use of it to determine the region of interest (ROI) of the masseter in the other MR slices belonging to the same data set. The upper and lower thresholds applied to these MR slices, for extraction of the masseter, are determined through the histogram of the 2-D segmented masseter. Visualization of the 3-D masseter is achieved via volume rendering. Our methodology has been applied to five MR data sets. Validation was done by comparing the segmentation results obtained by using our proposed methodology against manual contour tracings, obtaining an average accuracy of 83.5%
We propose a methodology that incorporates k-means and improved watershed segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread...
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ISBN:
(纸本)1424400694
We propose a methodology that incorporates k-means and improved watershed segmentation algorithm for medical image segmentation. The use of the conventional watershed algorithm for medical image analysis is widespread because of its advantages, such as always being able to produce a complete division of the image. However, its drawbacks include over-segmentation and sensitivity to false edges. We address the drawbacks of the conventional watershed algorithm when it is applied to medical images by using k-means clustering to produce a primary segmentation of the image before we apply our improved watershed segmentation algorithm to it. The k-means clustering is an unsupervised learning algorithm, while the improved watershed segmentation algorithm makes use of automated thresholding on the gradient magnitude map and post-segmentation merging on the initial partitions to reduce the number of false edges and over-segmentation. By comparing the number of partitions in the segmentation maps of 50 images, we showed that our proposed methodology produced segmentation maps which have 92% fewer partitions than the segmentation maps produced by the conventional watershed algorithm
In this paper, we propose a knowledge-based, fully automatic methodology for segmenting the masseter, which is a muscle of mastication, from 2-D magnetic resonance (MR) images for clinical purposes. To our knowledge, ...
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ISBN:
(纸本)1424400694
In this paper, we propose a knowledge-based, fully automatic methodology for segmenting the masseter, which is a muscle of mastication, from 2-D magnetic resonance (MR) images for clinical purposes. To our knowledge, there is currently no methodology which automatically segments the masseter from MR images. Our methodology uses five ground truths, where the masseter has been manually segmented and verified by medical experts, to serve as the reference and provide prior knowledge. The prior knowledge involved is the spatial relationship between the region of interest (ROI) of the head and ROI of the masseter. In the segmentation process, anisotropic diffusion first smoothens the ROI of the latter, and thresholding removes unwanted neighboring regions of the masseter. A template of the masseter is then used to obtain an initial segmentation of the muscle, which serves as the initialization to the gradient vector flow (GVF) snake for refining the initial segmentation. We performed 2-D segmentation of the masseter on a total of 25 MR images, which belong to the mid-facial region through the mandible from five data sets. Validation was done by comparing the segmentation results obtained by using our proposed methodology against manual segmentations done by medical experts, obtaining an average accuracy of 92%
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