基于多尺度植物种子图像边缘检测算法的设计与实现

小波变换,由于其具有时频局部化的特性及多尺度特性,能敏感地反映突变信号,是一种理想的边缘提取方法．本文系统地介绍了作者在图像边缘检测方面所做的理论探讨、算法及应用研究工作．目前的边缘提取方法有多种,本文将重点集中于基于小波变换的图像边缘检测方法的理论推导和算法实现．     

基于多尺度曲率植物叶片特征提取

基于B-样条小波计算边缘曲线多尺度曲率函数,根据多尺度信息筛选和定位超过一定曲率闲值的精角点,这样的点代表了边缘曲线的主要信息．文中使用了Canny边缘检测算子和数学形态学方法进行图像预处理,B-样条小波降低对噪声及扰动的灵敏性,以提高真实精角点定位水平．综合[1,2]给出新的角特征矢量,并生成角点特征序列CS和弧段特征序列SS．特征序列可作为自适应-时滞单元混合神经网络的输入,通过学习完成图像分类与识别,对基于植物叶片形状识别种类提供辅助。     

改良的小波变换算法在生物微粒检测中的应用

荧光图像的微粒检测已经成为了生物学研究中不可或缺的工具之一．介绍了一种改良的小波变换算法(improved wavelet transform,IWT),该方法实现简单,能够以很高的速度和精度来进行生物微粒的检测．IWT源自多尺度小波乘积算法(wavelet multiscale products,WMP),但它不仅解决了WMP算法遇到的问题,而且在处理各类图像的时候具有更强的适应性．使用人工合成的图像和真实的图像来定量地分析IWT、WMP以及多尺度方差稳定变换算法(multiscale variance stabilizing transform,MSVST)的检测效果．实验结果表明, IWT在大多数情况下的检测效果比WMP好很多,且与更为复杂的MSVST算法相当．此外,在处理相同图像时,IWT的速度比MSVST快20%．因此,IWT算法能够普遍适用于各种生物微粒的自动化检测,其简单准确的特点使之成为荧光图像分析更好的选择．     

基于PET医学图像边缘检测算法的研究

目的:边缘检测在图像处理中至关重要,可被广泛应用于目标区域识别、区域形状检测、图像分割等图像分析领域。边缘是图像中不平稳现象和不规则结构的重要表现,往往携带着图像中的大量信息,并给出图像轮廓。在医学图像三维显示技术中,为了更精确的临床判别需要得到单像素的清晰轮廓,因此我们提出一种新的边缘检测算法。方法:在传统的小波边缘检测的基础上,提出了一种新的边缘算法,即基于小波极大值边缘检测算法,应用模糊算法构造相应的隶属函数,再对得到的极大值进一步筛选。结果:将该算法应用到医学图像中,最终可以得到较清楚的单像素边缘轮廓,实验结果证明了该算法的可行性。结论:运用这种算法处理过的医学图像边缘锐化更好,更清晰,能够为肿瘤的早期识别提供依据,满足医学影像识别的需要。     

小波变换及其在医学图像处理中的应用

医学图像的好坏直接影响着医生对病情的诊断和治疗,因此利用数字图像处理等技术对医学图像进行有效的处理,已成为医学图像处理研究和开发的一大热点。小波变换是对傅里叶变换的继承和发展,在医学影像领域有着广泛的应用前景。本文介绍了二维离散小渡变换的一般形式,在图像分解与重构的基础上．系统地阐述了利用小小组变换的时频域特性与多分辨分析对医学图像进行去噪、增强以及边缘提取等深层次的处理,有效的改善图像质量。     

基于空间小波变换的生态地理界线识别与定位

为了提高生态地理分界线识别和定位的客观性,探讨了通过空间小波变换获取多尺度模极大值定位过渡带的方法.以NDVI和降水作为小波多尺度分解的对象,应用db3小波核函数分别对49条样带的模极大值进行了多尺度检测,并在GIS中确定其地理坐标.研究结果表明:识别半干旱半湿润生态地理分界线的最佳空间尺度为20～40 km,小于这一尺度定位过程容易受到局部地表覆被因素如城市区域或地形的影响,大于这一尺度由于要素被过度平滑,造成定位不准;从定位点的聚集度分析,NDVI的定位效果好于降水,特别是在较大空间尺度上.而与综合自然地理区划方案中的半干旱半湿润分界线比较,从定位点的方向性、平均最短距离以及均衡度三项指标综合判断,小波变换对于降水过渡带的定位优于对NDVI的定位.研究证实,空间小变换与GIS结合是提高生态地理分界线识别与定位科学性的重要途径,是对专家系统划分界线方法的有力补充和完善.     

强噪声,高基漂下的QRS检测器

本文描述了一种基于两进小波变换（ＤＹＷＴ）的ＱＲＳ波检测器。小波尺度的选择是基于心电信号的频谱的特点,并根据多尺度选择方法判决检测心电ＱＲＳ波,实验结果表明,对于在有强大的噪声和严重的基线漂移干扰下的心电信号能够有效的识别。     

应用小波变换检测细胞显微荧光图像中的囊泡

文章提出了一种用小波变换来检测生物荧光图像中囊泡的方法。作者用à trous小波对图像进行小波变换,然后求出每层系数的中值绝对偏差σ,并用t=kσ/0.67作为阈值对每层系数进行门限滤波,然后通过提取小波变换系数来重构图像。通过设计实验与常用的“rolling ball”算法对比,发现小波变换算法在低信噪比的情况下,具有更好的灵敏度;对于形状大小不同的信号,具有更好的稳定性;而且对于信号的细节信息具有更好的保真性。     

景观生态学中的尺度分析方法

多尺度空间分析法是发现和识别景观特征尺度的主要方法.当前这类方法很多,缺乏归类和对比分析评价.基于空间类型变量和数值变量,对多尺度空间分析方法进行了重新梳理.同时对当前常用的尺度分析方法:半方差分析、尺度方差分析、小波分析和孔隙度指数分析,以中国三北防护林为例,对比了各种尺度分析方法的特点和优劣.结果表明,在特征尺度的识别上:小波方差方法清晰明了;半方差分析法灵活简捷,结果明显;尺度方差分析法和孔隙度指数法在本研究中的判识结果不甚明显.在计算速度上:半方差分析法计算量最大、耗时最长,尺度方差次之,小波方差速度最快,孔隙度指数法计算速度快于前两种,慢于小波方差分析方法.半方差分析方法简单灵活,而且相关理论方法成熟,但缺乏对大尺度格局的整体把握,而小波分析恰恰能很好的弥补这一不足.最后提出,半方差分析和小波变换相结合将会是最优的尺度分析方法.     

初级视觉的小波表征及其图象压缩效果

初级视觉的Ｇａｂｏｒ函数模型和变换尺度为３是初级视觉处理外界信息的主要特征。在此基础上,由于小波所具有的多分辨特性与视觉处理由粗到细的过程相一致,因而,希望存在一类能够表征这两个初级视觉特征的小波变换。从这点出发,本文先给出了具有变换尺度为３的正交Ｈａａｒ基,而后给出了具有以上两个特征的小波基和小波滤波器。我们将其应用到图象信息的压缩上,着重与传统二进的小波变换图象压缩的效果进行了比较,结果说明具有视觉特征的小波变换能够提供良好的图象压缩效果和主观视觉效果     

Adaptive spatio-temporal denoising of fluoroscopic X-ray sequences

Lowering the cumulative radiation dose to a patient undergoing fluoroscopic examination requires efficient denoising algorithms. We propose a method, which extensively utilizes temporal dimension in order to maximize denoising efficiency. A set of subsequent images is processed and two estimates of denoised images are calculated. One is based on a special implementation of an adaptive edge preserving wavelet transform, while the other is based on the statistical method intersection of confidence intervals (ICI) rule. Wavelet transform is thought to produce high quality denoised images and ICI estimate can be used to further improve denoising performance about object edges. The estimates are fused to produce the final denoised image. We show that the proposed method performs very well and do not suffer from blurring in clinically important parts of images. As a result, its application could allow for significant lowering of the fluoroscope single frame dose.     

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.     

Multi‐ATOM: Ultrahigh‐throughput single‐cell quantitative phase imaging with subcellular resolution

A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost‐effective and label‐free cellular assays, which provides useful insights into understanding the biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single‐cell precision to define cell identities in a large and heterogeneous population of cells—hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multiplexed asymmetric‐detection time‐stretch optical microscopy (multi‐ATOM) that captures and processes quantitative label‐free single‐cell images at ultrahigh throughput without compromising subcellular resolution. We show that multi‐ATOM, based upon ultrafast phase‐gradient encoding, outperforms state‐of‐the‐art QPI in permitting robust phase retrieval at a QPI throughput of >10 000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi‐ATOM for large‐scale, label‐free, multivariate, cell‐type classification (e.g. breast cancer subtypes, and leukemic cells vs peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi‐ATOM could empower new strategies in large‐scale biophysical single‐cell analysis with applications in biology and enriching disease diagnostics.      

肝脏超声图象局部分形指数研究

为了研究人体组织超声图象的局部特征,并为进行人体组织定征研究提供新的参数,提出了一个分析超声图象局部分形指数的新方法－局部分形指数小波分析法ＬＦＷＡＭ（ＬｏｃａｌＦｒａｃｔａｌＳｃａｌｅＷａｖｅｌｅｔＡｎａｌｙｓｉｓＭｅｔｈｏｄ）。应用此法研究了人体肝脏组织超声图象分形体的构造规则;进行了局部分形指数的分析。验证了ＬＦＷＡＭ法分析肝脏超声图象局部特性的有效性,得出了局部分形指数更能全面、细致地刻画肝脏组织超声图象分形特征的结论,为进而研究局部病变的识别与图象的分割提供了基础。     

Fuzzy Logic Based Edge Detection in Smooth and Noisy Clinical Images

Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB ‘salt and pepper’ noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.     

Accurate measurements of protein interactions in cells via improved spatial image cross-correlation spectroscopy

The sensitive detection of protein interactions in living cells is an important first step toward understanding each of the multitude of cellular processes that are regulated by such interactions. Spatial image cross-correlation spectroscopy (ICCS) is one method used to measure protein-protein interactions from the analysis of two-channel fluorescence microscopy images. In spatial ICCS, cross-correlation of fluctuations in fluorescence intensity recorded as images from two independent wavelength detection channels in a fluorescence microscope is used to determine the average number of interacting particles in the imaged region. Even in situations where the particle number density is relatively high, ICCS provides an accurate measure of molecular interactions. However, it was shown previously that the method suffers from relatively high detection limits of interacting particles (approximately 20%) and can be perturbed by heterogeneous spatial distributions of the fluorescent particles within the images. Here, we demonstrate new approaches to circumvent some of the limitations of ICCS. Spatial scrambling of pixel blocks within fluorescence images was investigated as a way of extending the detection of spatial ICCS to measure lower interaction fractions as well as colocalization within cells. We also show that 'mean-intensity-padding' of regions of interest within fluorescence images is a feasible method of applying ICCS to arbitrarily selected areas of the cell with boundaries or edge morphologies that would be impossible to analyze with conventional ICCS. Using these newly developed strategies we were able to measure the fraction of actin that interacts with alpha-actinin in the leading edge of a migrating cell.     

Pré-traitement d’un volume IRM par filtrage anisotrope robuste pour la segmentation de l’épaule

A novel pre-treatment process for image segmentation, based on anisotropic diffusion and robust statistics, is presented in this paper. Image smoothing with edge preservation is shown to help upper limb segmentation (shoulder segmentation in particular) in MRI datasets. The anisotropic diffusion process is mainly controlled by an automated stopping function that depends on the values of voxel gradient. Voxel gradients are divided into two classes: one for high values, corresponding to edge voxels or noisy voxels, one for low values. The anisotropic diffusion process is also controlled by a threshold on voxel gradients that separates both classes. A global estimation of this threshold parameter is classically used. In this paper, we propose a new method based on a local robust estimation. It allows a better removing of noise while preserving edges in the images. An entropy criterion is used to quantify the ability of the algorithm to remove noise with different signal to noise ratios in synthetic images. Another quantitative evaluation criterion based on the Pratt Figure of Merit (FOM) is proposed to evaluate the edge preservation and their location accuracy with respect to a manual segmentation. The results on synthetic and MRI data of shoulder show the assets of the local model in terms of areas homogeneity and edges locations.