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Efficient image compression techniques for compressing multimodal medical images using neural network radial basis function approach 下载免费PDF全文
Perumal Balasubramani Pallikonda Rajasekaran Murugan 《International journal of imaging systems and technology》2015,25(2):115-122
Image compression technique is used to reduce the number of bits required in representing image, which helps to reduce the storage space and transmission cost. Image compression techniques are widely used in many applications especially, medical field. Large amount of medical image sequences are available in various hospitals and medical organizations. Large images can be compressed into smaller size images, so that the memory occupation of the image is considerably reduced. Image compression techniques are used to reduce the number of pixels in the input image, which is also used to reduce the broadcast and transmission cost in efficient form. This is capable by compressing different types of medical images giving better compression ratio (CR), low mean square error (MSE), bits per pixel (BPP), high peak signal to noise ratio (PSNR), input image memory size and size of the compressed image, minimum memory requirement and computational time. The pixels and the other contents of the images are less variant during the compression process. This work outlines the different compression methods such as Huffman, fractal, neural network back propagation (NNBP) and neural network radial basis function (NNRBF) applied to medical images such as MR and CT images. Experimental results show that the NNRBF technique achieves a higher CR, BPP and PSNR, with less MSE on CT and MR images when compared with Huffman, fractal and NNBP techniques. 相似文献
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Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression. This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network (IRBFNN). Particle swarm optimization (PSO) with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN. The performance of RBFNN is seriously affected by the centers of hidden neurons. Conventionally K-means was used to find the centers of hidden neurons. The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN. Thus, a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN. The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance. Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository. The proposed IRBFNN is compared with other variations of RBFNN, conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms. The proposed IRBFNN achieves an accuracy of 98.73%, 98.47% and 99.03% for three Parkinson’s datasets taken for experimentation. The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error. 相似文献
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关于颜色空间转换的RBF网络动态子空间自动划分辨识方法研究 总被引:1,自引:1,他引:0
以RGB与CIEL*a*b*颜色空间转换为例,采用径向基函数(RBF)神经网络,研究了颜色值在不同颜色空间之间的转换。利用基本采样数据集建立了颜色空间转换RBF网络模型,并通过增加样本数据,采用动态规划颜色子空间的方法,提高了模型转换精度。研究结果显示,该方法的转换速度和精度都优于基于动态子空间自动划分的BP神经网络颜色空间转换方法。 相似文献
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对超弹性形状记忆合金(SMA)丝在不同应变幅值和荷载速率下进行加卸载单轴拉伸试验,分析其滞回特性随环境因素的变化规律。将径向基函数神经网络(RBFNN)和Graesser模型结合起来,Graesser模型参数取自试验曲线,能由数学式确定的模型参数和应变幅值、荷载速率一起作为网络的输入信息,不能由数学式确定的模型参数作为输出神经元。数值计算表明,RBFNN可以精确地预测Graesser模型参数,且计算的SMA应力-应变曲线与Graesser模型结果吻合很好。 相似文献
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目的 构建一种新的径向基函数神经网络货架期预测模型。方法 研究不同贮藏温度下燕麦生鲜湿面的微生物、理化等指标的变化情况,通过Pearson相关性分析,筛选影响燕麦生鲜湿面货架期的主要因素,利用微生物生长动力学模型和径向基函数神经网络模型对燕麦生鲜湿面的剩余货架期进行预测。结果 微生物生长动力学模型不能很好地拟合燕麦生鲜湿面菌落总数的变化情况,预测精度较差,径向基函数神经网络预测模型的预测值与实际值的相对误差为2.66%。结论 径向基函数神经网络预测模型的效果较好,为以后食品货架期的预测提供了一定的参考依据。 相似文献
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目的 提出一种基于径向基函数(Radial Basis Function,RBF)神经网络的睡眠分期方法,设计一套能够根据用户身心恢复状态调节唤醒时间的智能唤醒系统,以优化用户睡眠时长,减轻醒后不适感。方法 基于心率变异性和睡眠分期等相关理论知识,通过低功耗心率带采集人体心电信号,选取最优小波变换对采集到的心电信号精准去噪,对径向基函数神经网络进行反复训练后,筛选出10个关键的特征向量,以构建睡眠分期模型。睡眠分期信息通过STM32处理器传输到手机客户端,系统根据预先设计的优化唤醒机制在用户身心恢复到最佳状态时将其唤醒。结果 基于睡眠分期模型的算法平均识别准确率可达88.9%,卡帕(Kappa)系数为0.839,相较于其他算法,该算法具有较高的准确率。结论 该智能唤醒系统的采集成本较低,算法简便高效,其唤醒机制科学合理,可以使用户舒适醒来,对改善用户醒后状态具有重要意义。 相似文献
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R. Noorossana M. Farrokhi A. Saghaei 《Quality and Reliability Engineering International》2003,19(6):493-504
This paper presents an artificial neural network model for detecting and classifying three types of non‐random disturbances referred to as level shift, additive outlier and innovational outlier which are common in autocorrelated processes. To the best of our knowledge, this is the first time that a neural network has been considered for simultaneous detection and classification of such non‐random disturbances. An AR (1) model is considered to characterize the quality characteristic of interest in a continuous process where autocorrelated observations are generated over time. The performance of the proposed procedure is evaluated through the use of a numerical example. Preliminary results indicate that the procedure can be used effectively to detect and classify unusual shocks in autocorrelated processes. Copyright © 2003 John Wiley & Sons, Ltd. 相似文献
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针对没有类似工程的新建工程项目,提出了投资估算的新方法.将新建工程项目分解为n项单位工程,然后利用反向传播神经网络从大量已完工程历史数据中"提取"类似单位工程,从非线性角度实现对具有类似项目的单位工程造价的预测;对没有类似项目的单位工程,分析其工程特征,将其区分为已知工程特征和未知工程特征,并利用已知工程特征和未知工程特征之间的经验或逻辑关系,建立模糊推理系统,使未知工程特征变为已知工程特征.根据工程特征与工程造价之间的经验或逻辑关系,建立模糊推理系统,计算得到没有类似项目的单位工程造价. 相似文献
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LIUQi-peng FENGQuan-ke XIONGWei 《国际设备工程与管理》2004,9(2):72-78
Fault diagnosis is confronted with two problems; how to “measure“ the growth of a fault and how to predict the remaining useful lifetime of such a failing component or machine. This paper attempts to solve these two problems by proposing a model of fault prognosis using wavelet basis neural network. Gaussian radial basis functions and Mexican hat wavelet frames are used as scaling functions and wavelets, respectively. The centers of the basis functions are calculated using a dyadic expansion scheme and a k-means clustering algorithm. 相似文献
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目的 解决传统印刷机、包装机各轴机械连接方式下,多电机同步控制存在的机械磨损严重、同步控制精度低等问题。方法 基于偏差耦合控制系统结构,将PID与神经网络控制相结合,提出一种径向基神经网络PID的多电机速度同步控制策略。结果 通过仿真可知,该算法相较于传统PID控制超调量更小,系统能够以较快的速度实现同步跟踪,速度同步精度明显高于PID控制。结论 该控制算法能够大大提升印刷机多轴同步控制的动态性能,可以有效提高印刷质量。 相似文献
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当图像中同时存在高斯噪声和椒盐噪声时,单一的均值滤波或中值滤波很难达到最佳滤波效果。 分析了噪声特点和各种滤波方法的优势,提出了一种基于神经网络的图像混合滤波及融合算法:首先建立概率神经网络,检测椒盐噪声和高斯噪声点,并分别利用中值滤波和均值滤波去除噪声点,然后建立径向基函数神经网络,利用训练好的径向基函数神经网络融合 2 种不同滤波的图像,输出理想的融合图像。 Matlab 仿真实验结果表明,该算法有效去除混合噪声的同时,能很好地保护图像的边缘与细节,是一种有效的方法。 相似文献
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使用RLE方法,能够快速提取印刷品缺陷的多种特征。针对不同缺陷特征,使用基于径向基神经网络的方法,依据缺陷目标的特征,对缺陷进行了分类。实验结果显示,径向基神经网络通过对训练样本的训练,对测试样本能够达到良好的分类效果。 相似文献
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基于粗糙集理论的模糊神经网络及其在化纤生产过程中的应用 总被引:5,自引:0,他引:5
提出一种基于粗糙集理论的模糊神经网络系统,首先运用粗糙集理论来发现大量样本数据中的概略化的规则,然后根据这些规则来设计神经网络的结构模型,并利用神经网络技术对模型进行训练。化纤工业中抽丝冷却侧吹风过程的模拟仿真实验,证明了该方法的有效性和可行性。 相似文献
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In this work, the dynamic model, flux-current-rotor position and torque-current-rotor position values of the switched reluctance
motor (SRM) are obtained in MATLAB/Simulink. Motor control speed is achieved by self-tuning fuzzy PI (Proportional Integral)
controller with artificial neural network tuning (NSTFPI). Performance of NSTFPI controller is compared with performance of
fuzzy logic (FL) and fuzzy logic PI (FLPI) controllers in respect of rise time, settling time, overshoot and steady state
error. 相似文献