结合方差稳定变换和PGPD去除磁共振图像Rician噪声

基于非局部自相似性块组学习的图像去噪(PGPD)算法去除高斯噪声效果优异,但是对于磁共振(MR)图像中Rician噪声的去除效果不理想。为此本文提出一种结合方差稳定变换和PGPD的新去噪算法FPGPD。该算法首先对含有Rician噪声的MR图像进行方差稳定变换,使噪声在变换域中近似服从高斯分布。用PGPD算法在变换域中去噪,最后经过方差稳定逆变换得到无偏去噪图像。理论分析和实验结果表明,FPGPD算法在去除MR图像中Rician噪声时比PGPD算法去噪性能好,具体体现为对图像细节和轮廓边缘保护得更好。     

一种新的改进Canny图像边缘检测算法

针对传统的Canny算法在平滑滤波时对图像边缘检测的影响,以及需要预先设定高低阈值等缺点,本文提出了一种新的Canny边缘检测算法。首先采用自适应中值滤波对图像进行处理;其次,添加45°和135°方向的梯度模板来计算梯度幅值;最后,采用Otsu算法计算经梯度幅值运算得到图像的高低阈值。实验结果表明,该改进的Canny算法具有良好的抗噪性能,并且能精确的检测出边缘信息。     

一种新的脑部血管重建方法

血管重建是神经外科导航软件中的重要组成部分。针对脑部血管可视化,目前存在两大问题：剔除头骨法得到的图像中血管和杂质组织并存、血管间断;血管分割法重建后的血管表面存在“失真面片”或“连接阶梯”。本工作采取对分割目标形状不敏感的阈值水平集法对血管进行分割,并结合ROI来提速,最后利用改进的二维距离场与Marching Cube算法结合进行血管表面重建。实验结果表明,该方法能很大程度消除“连接阶梯”并避免杂质组织出现和血管间断。同时,该方法可同时提取多根血管,而不需要考虑脑部血管复杂的拓扑结构,其重建结果可为医生诊断提供参考。     

基于加权相位分离的二维互相关弹性成像方法研究

弹性成像能够检测组织的弹性信息,进而描述组织生理、病理状态,对疾病的检测和诊断具有重要的应用价值。为了改善二维弹性算法的轴向分辨率,本文对一维和二维弹性成像算法进行了比较研究,提出一种基于加权相位分离和二维互相关的混合位移估计算法,首先利用二维时域互相关技术进行粗估计,然后再利用加权相位分离技术(WPS)对结果进行精估计。同时,通过仿真和仿体实验对算法的精确性和效率进行了验证。结果表明,算法具有较好的鲁棒性,能够有效地提高运行效率和图像信噪比,上述研究对高性能弹性成像系统的研究与设计具有重要的指导意义。     

Lab分通道直方图的彩色图像分割算法及应用

本文针对传统灰度直方图分割法未综合考虑图像色度及纹理特征、对灰度差异不明显或灰度范围重叠的图像出现过分割或欠分割等问题，提出一种新的基于Lab分通道直方图的彩色图像分割算法，引入具有序列不相关性的亮度L通道、红绿a通道及蓝黄b通道3种分割依据，通过Newton插值法进行拟合运算，可针对不同亮度、色度属性图像进行自由选择，并运用邻域灰度值相匹配原则解决相邻目标区域边缘像素的准确匹配问题，分局部、分形态、分区域实现图像中不同目标的提取。经验证，该法对区域亮度差异较大图像及区域色度差异显著于亮度差异图像的分割效果，均优于传统灰度直方图分割法，极大提升了直方图分割算法的适用性。将其与经典Reinhard色彩迁移算法结合，将源图像感兴趣目标区域经分通道分割后分别进行色彩迁移变换，较好解决了经典Reinhard算法对图像非目标区域的干扰、色彩误传及阶调层次损失严重等问题，突破传统迁移算法只能整体着色的局限性，实现分区域精准着色。     

基于分层策略的矿石颗粒图像区域分割

为了消除过渡带对矿石颗粒图像多区域分割的不利影响,提出了一种基于分层策略的自动分割方法。该方法共分为3部分:首先,采用分层策略去除过渡带,构建图像金字塔;其次,基于多层灰度直方图定位平坦区域的特征峰,生成初始标记;最后,通过基于标记的区域生长完成图像分割。实际图像的分割结果验证了方法的有效性。与通用的多阈值方法相比,本方法能获得更为准确的分割结果。     

人参皂苷正相薄层色谱二维分离的优化

 在薄层色谱分离人参皂苷的两种互补性的流动相组分比例与皂苷比移值呈相关关系的基础上 ,以薄层色谱分离中距离最小的两点之间的距离Dmin为优化标准 ,对人参皂苷的二维分离结果进行预测和优化。通过计算机扫描不同流动相组成下的Dmin值得到的三维网络图显示出的最大Dmin下的二维流动相的组成 ,即为达到最佳二维分离结果的流动相组成。以优化的流动相对皂苷进行二维展开 ,分离结果与预测结果吻合 ,且比一维展开分离出了更多的新的皂苷组分。     

图像和视频的快速去雾算法研究

雾、霾等恶劣天气会导致室外图像能见度和对比度降低。虽然可以通过增强有雾图像的对比度得到清晰的图像,但对比度的过度增强可能会截断像素值,造成信息丢失。因此,本文基于信息丢失问题提出了一种快速、优化的去雾算法。通过最小化信息丢失,使输出图像不仅能保留较多的细节,且具有较高的对比度。此外,通过将RGB颜色空间转换为YUV颜色空间,仅对亮度分量Y进行处理,提高了算法的运算速度。算法的对比实验结果表明,本文的算法不仅去雾效果明显,而且运算速度快,完全能满足视频去雾的实时性要求。     

移动窗口二维相关光谱技术

移动窗口二维相关光谱是一种新的二维相关分析方法,它将移动窗口的概念和二维相关分析方法有效地结合在了一起,利用移动窗口将庞大的光谱数据按矩阵分割成若干个便于操作的子矩阵,用二维相关光谱分别处理,将得到的结果综合分析,用以光谱变量和扰动变量为坐标的等高图表现出来,从中可以非常直观地观察出光谱强度在扰动变量方向上的变化,进而找出引起光谱强度突变的特征扰动点。本文主要介绍了移动窗口二维相关光谱的计算方法、基本特征、影响因素和实际应用,同时还详细介绍了以它为基础改进得到的扰动相关移动窗口二维相关光谱。扰动相关移动窗口二维相关光谱包括同步图和异步图,它不仅能很好地反映出引起光谱强度突变的特征扰动,还能详尽地描述出光谱强度在扰动过程中的变化情况。     

病理切片疫苗安全性指标自动检测技术EI北大核心CSCD

针对猪病灭活疫苗研发、生产过程中和疫苗病理安全性检测时,存在对油乳佐剂残留、炎症细胞聚集2大主要安全性指标评估人工统计不准确、效率低的问题,建立了一套小型病理切片的病理图像采集显微系统,并提出了针对疫苗安全性指标的定量分析算法,实现了对病理切片快速数字化图像采集,以及对待测指标的定量统计分析及检测结果的可视化标记。搭建了一套高分辨率病理图像采集显微系统,并建立了炎症细胞聚集区域病理图像分割模型和油乳佐剂疫苗残留区域病理图像分割模型。对整张数字病理图像进行窗口扫描,得到整张病理图像2大指标的分析结果并进行可视化视觉标注。结果表明,此检测系统能在数秒内完成病理切片的病理图像采集,并对关键疫苗病理安全性检测指标进行可视化标记和定量分析,对油佐乳剂残留像素检出覆盖率误差为0.015,检测像素交并比为0.89,炎症细胞聚集平均像素检出覆盖率误差为0.01,检测像素交并比为0.74,实现了对病理安全性指标高精度无干预的自动检测分析和标记,可以满足实际生产和研究的检测需求。     

Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis

BackgroundIn psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin’s surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging. However, for accurate disease diagnosis and severity detection, lesion segmentation has prime importance. In that context, our group had previously performed psoriasis lesion segmentation using the conventional clustering algorithm. However, it suffers from the constraint of falling into the local sub-optimal centroids of the clusters.ObjectiveThe main objective of this paper is to implement an optimal lesion segmentation technique with aims at global convergence by reducing the probability of trapping into the local optima. This has been achieved by integrating swarm intelligence based algorithms with conventional K-means and Fuzzy C-means (FCMs) clustering algorithms.MethodologyThere are a total of eight different suitable combinations of conventional clustering (i.e., K-means and Fuzzy C-means (FCMs)) and four swarm intelligence (SI) techniques (i.e., seeker optimization (SO), artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO)) have been implemented in this study. The experiments are performed on the dataset of 780 psoriasis images from 74 patients collected at Psoriasis Clinic and Research Centre, Psoriatreat, Pune, Maharashtra, India. In this study, we are employing swarm intelligence optimization techniques in combination with the conventional clustering algorithms to increase the probability of convergence to the optimal global solution and hence improved clustering and detection.ResultsThe performance has been quantified in terms of four indices, namely accuracy (A), sensitivity (SN), specificity (SP), and Jaccard index (JI). Among the eight different combinations of clustering and optimization techniques considered in this study, FCM + SO outperformed with mean JI = 0.83, mean A = 90.89, mean SN = 92.84, and mean SP = 88.27. FCM + SO found statistical significant than other approaches with 96.67 % of the reliability index.ConclusionThe results obtained reflect the superiority of the proposed techniques over conventional clustering techniques. Hence our research development will lead to an objective analysis for automatic, accurate, and quick diagnosis of psoriasis.     

Off-Line Metrology on SEM Images Using Gray Scale Morphology

Discrimination and metrology results of microlithographic patterns from top-down SEM images are explored by means of morphological image analysis. The method relies on the use of various morphological filters on a top down SEM image. The resulted images are segmented in order to derive a quality factor which discriminates the candidate images as under- or fully-developed. Furthermore, the fully developed images are processed in order to extract useful measurements. The proposed image analysis methodology achieves for first time, to the authors’ knowledge, successful off-line discrimination between under-developed and fully-developed cases. For the latter case, the measuring method relies upon the evaluation of the connected regions in the SEM image after segmentation. This is expressed by the Useful Threshold Range (UTR), which corresponds to that specific value of connected regions obtained for the wider range of the threshold. The method is experimentally demonstrated by employing 72 test images from high resolution patterns. The evaluated critical pattern parameters are found in good agreement to those derived from on-line procedures.     

Chemical reaction optimization for solving shortest common supersequence problem

Shortest common supersequence (SCS) is a classical NP-hard problem, where a string to be constructed that is the supersequence of a given string set. The SCS problem has an enormous application of data compression, query optimization in the database and different bioinformatics activities. Due to NP-hardness, the exact algorithms fail to compute SCS for larger instances. Many heuristics and meta-heuristics approaches were proposed to solve this problem. In this paper, we propose a meta-heuristics approach based on chemical reaction optimization, CRO_SCS that is designed inspired by the nature of the chemical reactions. For different optimization problems like 0-1 knapsack, quadratic assignment, global numeric optimization problems CRO algorithm shows very good performance. We have redesigned the reaction operators and a new reform function to solve the SCS problem. The outcomes of the proposed CRO_SCS algorithm are compared with those of the enhanced beam search (IBS_SCS), deposition and reduction (DR), ant colony optimization (ACO) and artificial bee colony (ABC) algorithms. The length of supersequence, execution time and standard deviation of all related algorithms show that CRO_SCS gives better results on the average than all other algorithms.     

A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model

Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization.     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

A modified fuzzy C‐means algorithm using scale control spatial information for MRI image segmentation in the presence of noise

The fuzzy C‐means (FCM) algorithm does not fully utilize the spatial information for image segmentation and is sensitive to the presence of noise and intensity inhomogeneity in magnetic resonance imaging (MRI) images. The underlying reason is that using a single fuzzy membership function the FCM algorithm cannot properly represent pattern associations to all clusters. In this paper, we present a modified FCM (mFCM) algorithm by incorporating scale control spatial information for segmentation of MRI images in the presence of high levels of noise and intensity inhomogeneity. The algorithm utilizes scale controlled spatial information from the neighbourhood of each pixel under consideration in the form of a probability function. Using this probability function, a local membership function is introduced for each pixel. Finally, new clustering centre and weighted joint membership functions are introduced based on the local membership and global membership functions. The resulting mFCM algorithm is robust to the noise and intensity inhomogeneity in MRI image data and thereby improves the segmentation results. The experimental results on a synthetic image, four volumes of simulated and one volume of real‐patient MRI brain images show that the mFCM algorithm outperforms k‐means, FCM and some other recently proposed FCM‐based algorithms for image segmentation in terms of qualitative and quantitative studies such as cluster validity functions, segmentation accuracy and tissue segmentation accuracy. Copyright © 2015 John Wiley & Sons, Ltd.     

An image analysis suite for spot detection and spot matching in two-dimensional electrophoresis gels

We propose a suite of novel algorithms for image analysis of protein expression images obtained from 2-D electrophoresis. These algorithms are a segmentation algorithm for protein spot identification, and an algorithm for matching protein spots from two corresponding images for differential expression study. The proposed segmentation algorithm employs the watershed transformation, k-means analysis, and distance transform to locate the centroids and to extract the regions of the proteins spots. The proposed spot matching algorithm is an integration of the hierarchical-based and optimization-based methods. The hierarchical method is first used to find corresponding pairs of protein spots satisfying the local cross-correlation and overlapping constraints. The matching energy function based on local structure similarity, image similarity, and spatial constraints is then formulated and optimized. Our new algorithm suite has been extensively tested on synthetic and actual 2-D gel images from various biological experiments, and in quantitative comparisons with ImageMaster2D Platinum the proposed algorithms exhibit better spot detection and spot matching.     

A density-based segmentation for 3D images,an application for X-ray micro-tomography

Density-based spatial clustering of applications with noise (DBSCAN) is an unsupervised classification algorithm which has been widely used in many areas with its simplicity and its ability to deal with hidden clusters of different sizes and shapes and with noise. However, the computational issue of the distance table and the non-stability in detecting the boundaries of adjacent clusters limit the application of the original algorithm to large datasets such as images. In this paper, the DBSCAN algorithm was revised and improved for image clustering and segmentation. The proposed clustering algorithm presents two major advantages over the original one. Firstly, the revised DBSCAN algorithm made it applicable for large 3D image dataset (often with millions of pixels) by using the coordinate system of the image data. Secondly, the revised algorithm solved the non-stability issue of boundary detection in the original DBSCAN. For broader applications, the image dataset can be ordinary 3D images or in general, it can also be a classification result of other type of image data e.g. a multivariate image.     

基于影像处理的干涉测量相位重构研究

随着计算机技术和光电成像元件的发展,影像信息处理技术已经在无损检测、医学检测以及干涉测量等领域得到广泛应用。本文主要研究了影像信息在干涉测量领域的波前相位提取方法。干涉条纹图是干涉测量领域中影像信息的载体,在干涉条纹处理方面,针对空域卡雷算法的特点,提出一种基于影像处理的单幅闭合干涉条纹图相位重构新算法。在空域卡雷算法处理方法的基础上,利用迭代修正算法对干涉图相位进行二次逼近,实现了对单幅干涉条纹图的高精度相位重构。Matlab仿真结果表明,迭代修正后的相位残差降低了25.8%,表明该算法在空域卡雷算法的基础上能够有效提高相位重构精度,实现干涉测量领域中影像信息的高精度处理。