基于信息量的脉冲耦合神经网络改进分割算法

目的解决区域生长简化脉冲耦合神经网络（PCNN）算法中由于阈值参数选取不当导致的分割不足与过分割问题。方法在区域生长简化PCNN算法中引入熵来刻画图像的信息量。结果避免了对阈值参数选取。结论基于信息量的PCNN改进算法在分割精度、算法的稳定性等方面均优于简化区域生长PCNN算法。     

基于简化的PCNN在超声乳腺癌图像去噪方面的应用

针对乳腺癌超声图像中斑点对诊断的影响,提出一种基于简化的脉冲耦合神经网络(simplified pulse-coupled neuralNetwork,SPCNN)的去噪新方法,并将此方法应用于乳腺癌超声图像滤波。首先利用简化的PCNN定位极端脉冲噪声点并利用中值滤波滤除椒盐噪声,然后利用PCNN赋时矩阵采用分类滤波自适应调节灰度值滤除高斯噪声。用实验图像验证了方法的有效性,然后将此方法应用于乳腺癌的超声图像中进行滤波,实验结果证实该方法对混合噪声在滤波效果和保护细节方面具有优势,对乳腺癌的超声图像能较好地滤除噪声,同时保证了细节,结合医学诊断证实了该方法的有效性。     

结合遗传算法的脉冲耦合神经网络在新生儿缺氧缺血性脑病磁共振图像中的应用研究

为收集新生儿缺氧缺血性脑病(HIE)核磁共振图像特征数据,采用基于遗传算法(GA)结合脉冲耦合神经网络(PCNN)的方法,对新生儿HIE磁共振图像进行分割实验和病灶特征提取。结果显示:基于GA的PCNN分割不仅有较好的分割结果,且优于具有固定参数PCNN的分割,可为HIE早期诊断系统建立提供依据,为进一步诊断及研究提供有效的帮助。     

噪声在脉冲耦合神经网络图像增强中的作用

神经系统中广泛存在着噪声,大量研究表明噪声有助于弱信号的检测和传输.脉冲耦合神经网络是建立在生物神经系统上的第三代人工神经网络,被广泛应用于图像处理.为了研究噪声对脉冲耦合神经网络图像处理的影响,通过在网络中引入加性噪声,用于图像增强.直观视觉效果和图像直方图均表明适当的噪声有助于图像增强,噪声过小或过强则减弱图像增强效果;图像的峰值信噪比随噪声强度增强呈现倒钟形,表明存在随机共振现象.本研究表明适当强度的噪声能够提高脉冲耦合神经网络图像处理的效果,并显示出随机共振,有助于开展基于生物神经系统的智能化图像处理方法的研究.     

MP-PCNN超声多普勒血流信号声谱图降噪去斑方法

为有效抑制超声多普勒血流信号声谱图中的背景噪声和多普勒斑点,提出了Matching Pursuit(MP)及单向衰减阈值脉冲耦合神经网络(MP-PCNN)模型。首先将分段的多普勒超声信号进行MP循环分解,分离噪声与信号,然后用单向衰减阈值PCNN模型计算声谱图在各个灰度等级上的点火时刻图并定位斑点,用中值滤波器抑制斑点。通过对各种信噪比的仿真超声多普勒血流信号处理,实验结果表明,MP-PCNN方法可有效地滤除声谱图中的噪声与斑点,并较好地保持边缘与细节信息,在主观及客观性能比较上优于同类降噪去斑方法。     

基于小波变换的医学图像去噪声处理

利用中值滤波和基于小波变换的去噪声处理对同时含有高斯噪声和脉冲噪声的X线图像降噪方法进行探讨.采用PSNR评价标准分析实验结果,表明小波变换结合中值滤波方法在去除噪声的同时较好地保持了原图像所包含的边缘信息,处理效果优于单纯的小波变换或单纯的中值滤波.     

一种融合表面波变换与脉冲耦合神经网络的三维肺实质分割算法

针对肺部病变及支气管干扰等因素导致的肺实质分割困难的问题,本文提出一种融合表面波(surfacelet)变换与脉冲耦合神经网络(PCNN)的肺实质分割算法。首先,通过surfacelet变换对三维肺部计算机断层扫描数据进行多尺度多方向分解,利用局部修正拉普拉斯算子选择处理后的子带系数增强图像的边缘特征;然后,经surfacelet逆变换得到增强后的图像作为PCNN的反馈输入;最后,通过循环迭代完成肺实质的分割。所提算法对公开数据集中的样本进行了测试。结果表明,本文算法的分割性能优于surfacelet变换边缘提取算法、三维区域生长算法和三维U形网络(U-NET)算法,能够有效抑制肺部病变及支气管的干扰,得到更完整的肺实质图像。     

基于PCNN自动波特征的血细胞图像分割和计数方法

在生物医学领域,由于细胞图像的低灰度、亮度的不均匀性以及细胞图像特有的复杂结构特性,使得细胞图像分割和计数非常困难.大量研究表明,PCNN非常适用于图像处理,本研究提出了一种基于PCNN自动波特征的血细胞图像分割和计数算法.首先运用PCNN对血细胞图像进行了降噪,分割等预处理,然后利用PCNN自动波的传播特性去除了细胞图像中的微小干扰物体,并通过对分割图像进行标记实现了对血细胞图像的准确计数和特定细胞的单独分割,实验结果表明,该方法非常有效.     

基于Contourlet变换和PCNN的CT图像椎体解剖轮廓特征提取方法的研究

提出一种新的基于Contourlet变换和脉冲耦合神经网络(PCNN)的医学图像解剖轮廓特征提取算法。首先对原始椎体CT图像进行Contourlet变换,得到能稀疏表示图像边缘以及方向信息的子带和低频子带;然后结合PCNN对低频子带进行边缘轮廓细节提取,最后利用处理后的所有子带系数,通过Contourlet逆变换,提取出图像的边缘轮廓。实验将本算法提取的结果与Canny算子、区域生长法以及结合小波变换和PCNN的算法提取的图像边缘轮廓进行比较,结果表明新算法能够有效的实现医学图像解剖结构轮廓特征的提取。     

基于生物突触可塑性的仿生分层脉冲神经网络事件相机对象识别系统EI北大核心

脉冲神经网络(SNNs)以稀疏脉冲时间编码、异步事件驱动的方式天然地适合处理事件相机输出的事件流数据。为了提高现有的仿生分层脉冲神经网络对事件相机对象的特征提取和分类性能,本文提出一种基于生物突触可塑性的仿生分层脉冲神经网络事件相机对象识别系统。该系统首先基于脉冲神经元电位对原始事件流进行自适应分割以提高系统计算效率,然后使用基于生物突触可塑性的仿生分层脉冲神经网络对事件流数据进行多层的时空特征提取并分类。在基于Gabor滤波器的事件驱动卷积层提取初级视觉特征之后,网络使用基于无监督脉冲时间依赖突触可塑性(STDP)规则的特征层提取频繁出现的显著特征,以及基于奖励调节STDP规则的特征层学习诊断性特征。本文提出的网络在四个基准事件流数据集上的分类精度均优于现有的仿生分层脉冲神经网络,并且本文方法对于较短的事件流输入数据也有很好的分类能力,对输入事件流噪声也具有较强的鲁棒性。综上,本文提出的网络能够提高该类网络对事件相机对象的特征提取和分类性能。     

Transient noise characterization and filtration in CCD cameras exposed to stray radiation from a medical linear accelerator

Charge coupled devices (CCDs) are being increasingly used in radiation therapy for dosimetric purposes. However, CCDs are sensitive to stray radiation. This effect induces transient noise. Radiation-induced noise strongly alters the image and therefore limits its quantitative analysis. The purpose of this work is to characterize the radiation-induced noise and to develop filtration algorithms to restore image quality. Two models of CCD were used for measurements close to a medical linac. The structure of the transient noise was first characterized. Then, four methods of noise filtration were compared: median filtering of a time series of identical images, uniform median filtering of single images, an adaptive filter with switching mechanism, and a modified version of the adaptive switch filter. The intensity distribution of noisy pixels was similar in both cameras. However, the spatial distribution of the noise was different: The average noise cluster size was 1.2 +/- 0.6 and 3.2 +/- 2.7 pixels for the U2000 and the Luca, respectively. The median of a time series of images resulted in the best filtration and minimal image distortion. For applications where time series is impractical, the adaptive switch filter must be used to reduce image distortion. Our modified version of the switch filter can be used in order to handle nonisolated groups of noisy pixels.     

Optimal digital filters for long-latency components of the event-related brain potential

A fundamentally important problem for cognitive psychophysiologists is selection of the appropriate off-line digital filter to extract signal from noise in the event-related brain potential (ERP) recorded at the scalp. Investigators in the field typically use a type of finite impulse response (FIR) filter known as moving average or boxcar filter to achieve this end. However, this type of filter can produce significant amplitude diminution and distortion of the shape of the ERP waveform. Thus, there is a need to identify more appropriate filters. In this paper, we compare the performance of another type of FIR filter that, unlike the boxcar filler, is designed with an optimizing algorithm that reduces signal distortion and maximizes signal extraction (referred to here as an optimal FIR filter). We applied several different filters of both types to ERP data containing the P300 component. This comparison revealed that boxcar filters reduced the contribution of high-frequency noise to the ERP but in so doing produced a substantial attenuation of P300 amplitude and, in some cases, substantial distortions of the shape of the waveform, resulting in significant errors in latency estimation. In contrast, the optimal FIR filters preserved P300 amplitude, morphology, and latency and also eliminated high-frequency noise more effectively than did the boxcar filters. The implications of these results for data acquisition and analysis are discussed.     

基于自适应邻域的超声图像加权中值滤波

提出了一种自适应邻域中值滤波算法，用于医学超声内窥镜图像的噪声滤除。该方法以图像象素邻域的灰度方差为阈值，进行保持与修复窗口的自适应改变，在有效抑制Speckle噪声的同时，较好保留了图像的细节信息。对本算法与Loupas提出的加权中值滤波算法进行了比较，指出本算法在一定程度上克服了加权中值滤波器的不足并保留了它的优点，对超声内窥镜图像的滤噪有较好的效果。     

冠状动脉造影图像降噪处理的3种方法比较

目的 对冠状动脉造影图像降噪处理的3种方法进行比较。方法 将冠状动脉造影图像数字化并输入计算机，然后再用中值滤波法，自适应滤波法和基于小波变换的降噪处理3种方法分别处理同一图像，比较效果。结果 成功地运用了3种方法对冠状动脉造影图像进行降噪处理，图像质量均有所提高。结论 自适应降噪处理和基于小波系数的降噪处理结果较好，但自适应降噪处理的速度要快。     

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 filters 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.     

Automatic segmentation algorithm for the extraction of lumen region and boundary from endoscopic images

A new segmentation algorithm for lumen region detection and boundary extraction from gastro-intestinal (GI) images is presented. The proposed algorithm consists of two steps. First, a preliminary region of interest (ROI) representing the GI lumen is segmented by an adaptive progressive thresholding (APT) technique. Then, an adaptive filter, the Iris filter, is applied to the ROI to determine the actual region. It has been observed that the combined APT-Iris filter technique can enhance and detect the unclear boundaries in the lumen region of GI images and thus produces a more accurate lumen region, compared with the existing techniques. Experiments are carried out to determine the maximum error on the extracted boundary with respect to an expert-annotated boundary technique. Investigations show that, based on the experimental results obtained from 50 endoscopic images, the maximum error is reduced by up to 72 pixels for a 256 × 256 image representation compared with other existing techniques. In addition, a new boundary extraction algorithm, based on a heuristic search on the neighbourhood pixels, is employed to obtain a connected single pixel width outer boundary using two preferential sequence windows. Experimental results are also presented to justify the effectiveness of the proposed algorithm.     

一种基于自适应的新滤波技术

在心电信号的采集过程中,不可避免地会混入肌电噪声和各种干扰信号,为节获得含有较小噪声的ＥＣＧ信号,便于分析,需要对采集到的ＥＣＧ信号作消噪处理。     

基于对数域连续小波变换电路的心电图QRS波检测

为满足植入式心脏起搏器之类的医疗设备低功耗、实时处理等应用要求的需要,提出了基于低电压、低功耗对数域连续小波变换电路的心电图QRS波检测方法。为便于用模拟VLSI实现小波变换,用混合粒子群算法构造了类高斯一阶导数小波。以平衡式对数域积分器为积木块,设计了用于QRS波检测的连续小波变换电路,该电路由冲激响应为类高斯一阶导数小波函数的反褶及其伸缩的滤波器组构成。由该电路实现心电信号的小波变换,进行QRS波检测。仿真结果表明了该方法的可行性。     

基于数学形态学的心音包络提取与识别方法研究

心音包络比原始心音可以更好地显示心音的特征,是进行心音独立识别的基础.本文把数学形态学应用于心音包络的提取和识别的研究.首先利用形态学滤波和全波整流对原始心音进行预处理;然后利用形态学闭运算提取心音包络;最后应用形态学开运算来消除噪声包络.在数学形态学提取的心音包络基础上,对50例心音样本进行了第一心音、第二心音识别,全部20例正常心音的第一心音和第二心音被正确识别,27例包含心杂音的异常心音的第一心音、第二心音也被正确识别.为进一步的心音分析及心音诊断奠定了基础.