超声医学图像滤波算法研究进展

主要讨论超声医学图像滤波算法的研究现状,几种主要的滤波方法（多方位滤波方法、自适应权值调节滤波方法、自适应窗口选取滤波方法、两步法等）面临的问题及发展的方向。作者通过实践,将有关算法应用于超声医学图像的处理,给出了处理结果,进行了几种算法的比较分析。     

基于形态学重构的超声医学图像滤波方法

Speckle噪声是造成超声医学图像质量下降的最主要原因。我们通过修改形态学重构算法-Downhill算法的初始条件,使其适用于超声医学图像的去噪处理。首先在掩模图像中确定标记图像作为算法的初始化和开始区域,再使用改进的Downhill算法对超声医学图像进行滤波处理。实验结果表明,与其他3种传统滤波方法相比,该方法能快速有效地去除心脏腔室内的Speckle噪声同时保留图像的轮廓细节信息。     

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。     

产前超声医学图像处理技术综述

超声诊断是产科临床上应用最广泛的医学成像方式,与之相应的自动医学图像处理是提高诊断准确率与客观性的重要手段.本文首先介绍超声医学图像处理方法的原理及特点,讨论了若干技术及其涉及的算法,包括图像滤波、图像分割及机器学习技术等,浅析了设计可靠的超声医学图像处理方法的要点.其次,以产前超声医学为背景介绍了这些技术的应用,主要包括标准切面自动提取和生物学参数自动测量.最后,讨论了产前超声智能化诊断的发展方向.     

基于各向异性散布的医学图像非线性滤波法

为了向临床医生提供清晰准确的诊断依据 ,在对医学断层图像 (CT、MRI)进行滤波处理时 ,须保留具有重要诊断意义的微细结构。然而 ,绝大多数滤波技术在去噪的同时却滤出了细小结构。本文介绍了一个改进的非线性各向异性散布滤波算法 ,图像滤波被认为是一种散布迭代过程 ,通过自动确定最优散布常数和迭代次数 ,散布过程在遇到边界时就会被抑制或停下来 ,从而保留了边缘信息和微小结构。通过对实际医学图像(CT、MRI)的实验表明 ,本文算法既能提高信噪比又可保留重要的解剖结构     

基于低通和非线性滤波的MR图像增强算法

利用图像高频分量的局部特征，构造一种新的非线性滤波剪切阈值，使整幅图像的增强趋于均匀化。结合平滑去噪和非线性滤波图像增强两个步骤，我们提出了一种实用的MR图像增强算法。试验结果表明增强后MR图像主观视觉效果显著改善。     

基于局部与非局部医学图像降噪技术的研究进展

医学图像对临床诊断具有重要的参考价值,为了获得高质量的医学图像,达到清晰辨识病理区域特征的目的,降噪技术成为重要的研究领域。首先,简要介绍医用设备成像原理及产生的噪声类型。然后,以局部、非局部为分类阐述几种常用的降噪方法,重点介绍非局部均值滤波算法的原理、优缺点、改进方式及应用范围。最后,总结上述降噪技术存在的主要问题,展望今后的研究方向,非局部与局部滤波技术结合运用可能为一种通用的医学图像降噪技术带来新思路。     

基于光学相干层析离体牙图像的去噪算法研究

目的光学相干层析成像因其高分辨率、无损等优点,适于早期龋检测;但由于系统中存在的噪声,影响其成像质量。为了重建牙齿的原貌信息,需寻找一种适于早期龋检测的光学相干层析成像实时图像去噪算法。方法比较平均曲率流滤波、非线性扩散拉普拉斯金字塔算法、非局部均值滤波3种滤波方法对光学相干层析人离体牙图像的去噪效果,从噪声抑制、边界保持、运算时间3方面分析上述3种算法的实时去噪性能及其优缺点。结果非局部均值滤波在噪声抑制和边界保持2个方面能达到很好的平衡,但实时性差;而非线性扩散拉普拉斯金字塔算法则能在滤波效果和运算效率达到较好的平衡;平均曲率流滤波次之。结论非线性扩散拉普拉斯金字塔算法较适于早期龋检测的光学相干层析成像实时图像去噪。     

正电子发射断层成像重建算法评述

正电子发射断层成像（PET）是核医学中最为重要的应用之一，也是科学研究的强有力的工具，本文综述了PET图像重建方法研究现状，并着重介绍了滤波反投影算法和期望最大算法的最新进展。     

一种改进的灰度医学图像平滑滤波算法

提出了一种改进的灰度医学图像平滑滤波算法,此算法既有效地滤除了医学图像中的噪声,又能很好的保持了图像的边缘及细节,也克服了作者先前所提算法滤波后图像边缘出现毛刺的缺点。     

基于神经网络的波型检测方法

介绍了一种基于神经网络白化匹配滤波器的ＱＲＳ波检测方法。我们用神经网络白化匹配滤波器来处理ＥＣＧ信号的低频成分,模拟其非线性及非稳态的特性。处理后的信号中含有ＥＣＧ中大部分高频成分,让其通过一线性匹配滤波器来检测ＱＲＳ波及其位置。对于大噪声的ＥＣＧ信号,在匹配滤波器后加差分滤波,取平方及滑动平均等处理,提高检测正确率。使用这种方法我们对ＭＩＴ／ＢＩＨ心电信号数据库中噪声比较大的１０５号数据进行的处     

Digital Inverse Filtering Methodology For Physiological Signals

The use of digital inverse filtering for modifying the properties of physiological signals is discussed. Particular reference is made to phase correction of signals distorted by recording with analog filters having nonlinear phase properties. Some practical considerations in implementing digital filtering and inverse filtering are presented, especially problems of time-domain and frequency-domain resolution, inverse filter definition, and the effects of digitization errors.     

An adaptive panoramic filter bank as a qualitative model of the filtering system of the cochlea: the peculiarities in linear and nonlinear mode

Outer hair cells in the cochlea of the ear, together with the local structures of the basilar membrane, reticular lamina and tectorial membrane constitute the adaptive primary filters (PF) of the second order. We used them for designing a serial-parallel signal filtering system. We determined a rational number of the PF included in Gaussian channels of the system, summation weights of the output signals, and distribution of the PF along the basilar membrane. A Gaussian panoramic filter bank each channel of which consists of five PF is presented as an example. The properties of the PF, the channel and the filter bank operating in the linear and nonlinear modes are determined during adaptation and under efferent control. The results suggest that application of biological filtering principles can be useful for designing cochlear implants with new speech encoding strategies.     

Application of anisotropic diffusion to digital enhancement of portal images

We propose the use of anisotropic diffusion filtering to remedy difficulties in analysis of electronic portal images, stemming from their low contrast and high noise levels. Anisotropic diffusion is a nonlinear filter based on the numerical solution to the partial differential equation describing the process of diffusion. In this study we show that this filter is capable of greatly reducing noise in homogeneous areas of portal images while preserving the edges and contrast associated with anatomical features. We also demonstrate that the application of anisotropic diffusion leads to more consistent and reproducible visual extraction of features from portal images.     

Noise and baseline wandering suppression of ECG signals by morphological filter

Electrocardiogram (ECG) signals describe the electrical activity of the heart, and are universally by physicists in the diagnosis of cardiac pathologies. However, during the acquisition of ECGs they are often contaminated with different sources of noise, making interpretation difficult. Different techniques have been used to filter the ECG signal, in order to optimize the signal to noise ratio (S/N). In this paper, an approach based on morphological filtering is developed in order to filter the ECG. Morphological filtering is concerned with the detection of the ECG morphology, therefore allowing the suppression of noises and particularly baseline wandering. The implemented filter is evaluated using signals taken from the MIT-BIH ECG universal database. The results show that the performance of this filter is good compared with other filtering techniques.     

基于高频中值滤波的小波滤波在脑动脉色素浓度谱特征信号提取中的应用

近年来,近红外光谱技术(mear infrared spectrometry,NIRS)在脑科学研究领域倍受青睐。为了更好地满足参数的后续使用并提取出有效特征信号,前期尝试了多种常用滤波方法,为了解决滤后信号易失真以及不能有效滤除低频或者高频噪声的问题,提出了基于高频中值滤波的小波滤波,并采用高度还原真实信号特点的仿真信号以及利用脑血流动力学参数采集系统获得的真实光电容积脉搏波(photoplethysmography,PPG)信号,基于中值滤波的小波滤波进行分析。将实验测试数据与其他滤波方法的特征信号提取效果进行对比,并对处理数据进行信噪比和频谱分析。结果表明,采用中值滤波与小波滤波相结合的滤波方式,对脑动脉色素浓度谱特征信号进行滤波处理,能获得有效、精准的脑血流动力学参数,为后续的测量精度打下基础。该方法有效结合了中值滤波能够剔除粗差的特性和小波滤波在光电容积脉搏波中有效滤除高斯信号的特性,改善了采用单一方式的局限性,提供了一种新的PPG滤波的思路,对比传统方法更加优化。     

Physiologically inspired signal preprocessing for auditory prostheses: insights from the electro-motility of the OHC

We designed a non-linear functional model of the outer hair cell (OHC) functioning in the filtering system of the cochlea and then isolated from it two second-order structures, one employing the mechanism of the somatic motility and the other the hair bundle motion of the OHC. The investigation of these circuits showed that the main mechanism increasing the sensitivity and frequency selectivity of the filtering system is the somatic motility. The mechanism of the active hair bundle motion appeared less suitable for realization of the band-pass filtering structures due to the dependence of the sensitivity, natural frequency and selectivity on the signal intensity. We combined three second-order filtering structures employing the mechanism of the somatic motility and the lateral inhibition to form a parallel-type filtering channel of the sixth order with the frequency characteristics of the Butterworth-type and Gaussian-type. The investigation of these channels showed that the Gaussian-type channel has the advantage over the Butterworth-type channel. It is more suitable for realization of a filter bank with common lateral circuits and has less distorted frequency characteristic in the nonlinear mode.     

Nonlinear optical Fourier filtering technique for medical image processing

Real-time nonlinear optical Fourier filtering for medical image processing is demonstrated, exploiting light modulating characteristics of thin films of the biophotonic material bacteriorhodopsin (bR). The nonlinear transmission of bR films for a 442 nm probe beam with a 568 nm control beam and vice versa is experimentally studied in detail. The spatial frequency information carried by the blue probe beam is selectively manipulated in the bR film by changing the position and intensity of the yellow control beam. The feasibility of the technique is first established with different shapes and sizes of phantom objects. The technique is applied to filter out low spatial frequencies corresponding to soft dense breast tissue and displaying only high spatial frequencies corresponding to microcalcifications in clinical screen film mammograms. With the aid of an electrically addressed spatial light modulator (SLM), we successfully adapt the technique for processing digital phantoms and digital mammograms. Unlike conventional optical spatial filtering techniques that use masks, the technique proposed can easily accommodate the changes in size and shape of details in a mammogram.     

Time-varying digital filtering of ECG baseline wander

Time-varying filtering techniques are applied to the problem of baseline correction by letting the cut-off frequency of a linear filter be controlled by the low-frequency properties of the ECG signal. The time-varying filter is implemented as a bank of linear low-pass filters, in which each filter has a slightly differing cut-off frequency. Sampling rate decimation and interpolation are employed because the design of a filter for baseline reduction can be treated as a narrowband filtering problem. All filters have a linear phase response to reduce, for example, ST-segment distortion. The performance of the technique presented is studied on ECG signals with different types of simulated baseline wander. The results are compared with the performance of time-invariant linear filtering and cubic spline interpolation. The results show that an improvement in performance can be achieved when using time-varying filtering, especially at low heart rates or during episodes with excessive baseline wander.     

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.