结合光谱和形态剖面特征自适应加权影像分类

针对遥感影像分类中多特征利用的问题,提出一种综合利用光谱和形态剖面特征的分类方法.该方法首先将原始影像经形态属性滤波器滤波所得的形态剖面特征和影像光谱值一起组成特征向量;然后依据训练样本各特征分量的方差确定不同地物类别相应特征分量的初始权重,并通过训练样本的特征加权获得各类别的初始中心;随后,根据初始权重计算每个训练样本到各聚类中心的加权距离,将最小距离对应的类别作为训练样本的初始类别;再对每一类别,根据该类训练样本中那些被错分为其他类别样本的均值与该类初始中心的距离来调整该类初始权重;最后,根据调整后的权重,采用加权距离最小分类方法对整个影像进行分类.实验结果表明,形态剖面特征有效地弥补了光谱信息区分能力不足的缺点,调整后的权重强化了区分能力强的特征分量.     

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Image segmentation aims to determine structures of interest inside a digital picture in biomedical sciences. State‐of‐the art automatic methods however still fail to provide the segmentation quality achievable by humans who employ expert knowledge and use software to mark target structures on an image. Manual segmentation is time‐consuming, tedious and suffers from interoperator variability, thus not serving the requirements of daily use well. Therefore, the approach presented here abandons the goal of full‐fledged segmentation and settles for the localization of circular objects in photographs (10 training images and 20 testing images with several hundreds of nuclei each). A fully trainable softcore interaction point process model was hence fit to the most likely locations of nuclei of meningioma cells. The Broad Bioimage Benchmark Collection/SIMCEP data set of virtual cells served as controls. A ‘colour deconvolution’ algorithm was integrated to determine (based on anti‐Ki67 immunohistochemistry) which real cells might have the potential to proliferate. In addition, a density parameter of the underlying Bayesian model was estimated. Immunohistochemistry results were ‘simulated'for the virtual cells. The system yielded true positive (TP) rates in the detection and classification of real nuclei and their virtual counterparts. These hits outnumbered those obtained from the public domain image processing software ImageJ by 10%. The method introduced here can be trained to function not only in medicine and morphology‐based systems biology but in other application domains as well. The algorithm lends itself to an automated approach that constitutes a valuable tool which is easy to use and generates acceptable results quickly.     

A new texture and shape based technique for improving meningioma classification

Over the past decade, computer‐aided diagnosis is rapidly growing due to the availability of patient data, sophisticated image acquisition tools and advancement in image processing and machine learning algorithms. Meningiomas are the tumors of brain and spinal cord. They account for 20% of all the brain tumors. Meningioma subtype classification involves the classification of benign meningioma into four major subtypes: meningothelial, fibroblastic, transitional, and psammomatous. Under the microscope, the histology images of these four subtypes show a variety of textural and structural characteristics. High intraclass and low interclass variabilities in meningioma subtypes make it an extremely complex classification problem. A number of techniques have been proposed for meningioma subtype classification with varying performances on different subtypes. Most of these techniques employed wavelet packet transforms for textural features extraction and analysis of meningioma histology images. In this article, a hybrid classification technique based on texture and shape characteristics is proposed for the classification of meningioma subtypes. Meningothelial and fibroblastic subtypes are classified on the basis of nuclei shapes while grey‐level co‐occurrence matrix textural features are used to train a multilayer perceptron for the classification of transitional and psammomatous subtypes. On the whole, average classification accuracy of 92.50% is achieved through the proposed hybrid classifier; which to the best of our knowledge is the highest. Microsc. Res. Tech. 77:862–873, 2014. © 2014 Wiley Periodicals, Inc.     

Feature enhancement framework for brain tumor segmentation and classification

Automatic medical image analysis is one of the key tasks being used by the medical community for disease diagnosis and treatment planning. Statistical methods are the major algorithms used and consist of few steps including preprocessing, feature extraction, segmentation, and classification. Performance of such statistical methods is an important factor for their successful adaptation. The results of these algorithms depend on the quality of images fed to the processing pipeline: better the images, higher the results. Preprocessing is the pipeline phase that attempts to improve the quality of images before applying the chosen statistical method. In this work, popular preprocessing techniques are investigated from different perspectives where these preprocessing techniques are grouped into three main categories: noise removal, contrast enhancement, and edge detection. All possible combinations of these techniques are formed and applied on different image sets which are then passed to a predefined pipeline of feature extraction, segmentation, and classification. Classification results are calculated using three different measures: accuracy, sensitivity, and specificity while segmentation results are calculated using dice similarity score. Statistics of five high scoring combinations are reported for each data set. Experimental results show that application of proper preprocessing techniques could improve the classification and segmentation results to a greater extent. However, the combinations of these techniques depend on the characteristics and type of data set used.     

基于光谱能量分析的介质厚度测量方法

介绍了一种对厚度很小的平行玻璃平板厚度的高精度测量方法。通过激光束垂直照射被测物,用光谱仪接受并分析反射光各波长的能量。以此数据分析出介质对不同波长光线的反射率,找出反射率极大的波长。使用这些具有反射极大的波长进行计算,就得到介质的厚度值。该测量方案结构简单,测量精度较高。     

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.     

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.     

Automated classification of wear particles based on their surface texture and shape features

In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.     

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the conventional neural network based models usefully connected or convolutional neural networks to perform segmentation and classification. In this research, we present deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images. The two different models, that is, ConvNet and LSTM networks are trained using the same data set and combined to form an ensemble to improve the results. We used publicly available MICCAI BRATS 2015 brain cancer data set consisting of MRI images of four modalities T1, T2, T1c, and FLAIR. To enhance the quality of input images, multiple combinations of preprocessing methods such as noise removal, histogram equalization, and edge enhancement are formulated and best performer combination is applied. To cope with the class imbalance problem, class weighting is used in proposed models. The trained models are tested on validation data set taken from the same image set and results obtained from each model are reported. The individual score (accuracy) of ConvNet is found 75% whereas for LSTM based network produced 80% and ensemble fusion produced 82.29% accuracy.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.     

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.     

"Derived spectra" software tools for detecting spatial and spectral features in spectrum images

A spectrum image is recorded as an x-y array of beam locations at each of which a spectrum of radiation is recorded as stimulated by the beam. The large database or "datacube" that results from a single image presents a significant challenge to the analyst to recover information efficiently, especially in the case where a true unknown is examined. This paper describes a class of "derived spectra" software tools that can aid the analyst in recognizing both common and rare features within the datacube. A derived spectrum tool creates a spectrum-like display (intensity vs. channel number) in which the intensity (e.g., x-ray counts) at a particular channel (e.g., x-ray photon energy) is calculated from all of or a subset of the pixel intensities measured for that channel. Derived spectra tools considered include the SUM, MAXIMUM PIXEL, RUNNING SUM, and RUNNING MAXIMUM. The SUM-derived spectrum is useful for recognizing common features of the datacube, while the MAXIMUM PIXEL-and RUNNING MAXIMUM-derived spectra can locate rare, unanticipated features, which may occur as infrequently as being present at a single pixel in the original datacube.     

传感器阵列预测空域多信号分类目标定位跟踪

目标定位跟踪技术广泛应用于军事民用领域,是当前研究的热点与难点.提出了一种空域多信号分类-自回归粒子滤波(multiple signal classification autoregressive particle filter,MUSIC-ARPF)方法,定位跟踪地面目标.该方法使用多信号分类(multiple signal classification,MUSIC)算法估计目标波达方向(direction of arrival,DOA)并计算目标信号源位置,利用自回归(autoregressive model,AR)模型和粒子滤波(particle filter,PF)算法预测信号源下一时刻位置,进而自适应选择通带与阻带扇面进行空域滤波,同时调整MUSIC算法中谱峰搜索区域,提高DOA估计的分辨率,减少目标定位的扫描域.实验结果表明,空域MUSIC-ARPF方法能够减少目标定位时间,提高目标跟踪精度.     

An automated feedback system with the hybrid model of scoring and classification for solving over-segmentation problems in RNAi high content screening

Background: High content screening (HCS) via automated fluorescence microscopy is a powerful technology for generating cellular images that are rich in phenotypic information. RNA interference is a revolutionary approach for silencing gene expression and has become an important method for studying genes through RNA interference‐induced cellular phenotype analysis. The convergence of the two technologies has led to large‐scale, image‐based studies of cellular phenotypes under systematic perturbations of RNA interference. However, existing high content screening image analysis tools are inadequate to extract content regarding cell morphology from the complex images, thus they limit the potential of genome‐wide RNA interference high content screening screening for simple marker readouts. In particular, over‐segmentation is one of the persistent problems of cell segmentation; this paper describes a new method to alleviate this problem. Methods: To solve the issue of over‐segmentation, we propose a novel feedback system with a hybrid model for automated cell segmentation of images from high content screening. A Hybrid learning model is developed based on three scoring models to capture specific characteristics of over‐segmented cells. Dead nuclei are also removed through a statistical model. Results: Experimental validation showed that the proposed method had 93.7% sensitivity and 94.23% specificity. When applied to a set of images of F‐actin‐stained Drosophila cells, 91.3% of over‐segmented cells were detected and only 2.8% were under‐segmented. Conclusions: The proposed feedback system significantly reduces over‐segmentation of cell bodies caused by over‐segmented nuclei, dead nuclei, and dividing cells. This system can be used in the automated analysis system of high content screening images.     

基于广义随机Petri网的可重组制造单元建模与分析方法

为揭示可重组制造单元的重组过程并计算重组方式对系统性能的影响,根据可重组制造单元的特征,提出了基于广义随机Petri网的模块化建模及分析方法。该方法将制造单元的不同资源及重组生产过程对应于相应的广义随机Petri网基本模块,并通过过渡变迁合成广义随机Petri网模型。在此基础上,采用马尔可夫理论及相关数学方法,分析可重组制造系统的性能指标,并通过实际应用,验证了该建模与分析方法的有效性。     

基于VMD和互谱分析的供水管道泄漏定位方法

针对低信噪比(SNR)下的供水管道泄漏振动信号用于时延估计泄漏定位误差大的问题,提出基于变分模态分解(VMD)和互谱分析结合的供水管道泄漏定位方法。首先,利用VMD将管道泄漏信号分解为若干个本征模态函数(IMF),对供水管道泄漏信号进行互谱分析确定特征频带;然后,利用IMF分量在特征频带内的能量比例作为选取准则来确定有效IMF分量,并对选取的有效IMF分量进行重构;最后,对重构信号进行时延估计来确定泄漏点位置。为了验证所提泄漏定位算法的有效性,通过仿真和实验分别对互谱与VMD结合、互相关及VMD与相关系数结合3种方法进行研究。实验结果表明,以上3种定位算法的平均相对定位误差分别为2. 53%,8. 62%和16. 86%。     

Automated recognition and mapping of immunolabelled neurons in the developing brain

The cerebral cortex is distinguished by layers of neurons of different morphologies and densities. The layers are formed by the migration of newly generated neurons from the ventricular zone to the cortical plate near the outer (pial) boundary of the cortex, along radial paths approximately perpendicular to the cortical surface. Immunochemical labelling makes these cells' patterns visible in brightfield microscopy so that layer formation can be studied. We developed a suite of programs that automatically digitize the entire cortex, identify the labelled cells and compute cell densities along local radial paths. Cell identification used supervised classification on all the significantly stained objects corresponding to maxima in lowpass filtered versions of the digital micrographs. Classification of all the stained objects as cells or noncell objects was made by a decision rule based on morphometric and grey-level texture features, including features based on Gabor functions. Detection sensitivity and classification accuracy were jointly maximized on training data consisting of about 3000 expert-identified neurons in micrographs. Total program performance was tested on a separate (test) set of labelled neurons the same size as the training data set. The program detected 85% of the cells in the test set with a total error of 019. The identified cells' locations were used to compute population densities along normals to the cortical layers, and these densities served as a measure of neuronal migration. Transcortical density profiles obtained by computation and by manual cell counting were very similar. The cell identification program was built on well-established methods in statistical pattern recognition and image analysis and should generalize readily to other histological preparations.     

A novel measurement method for transient detection based in wavelets entropy and the spectral kurtosis: An application to vibrations and acoustic emission signals from termite activity

This paper presents a novel non-destructive method for termite detection that uses the entropy of the continuous wavelet transform of the acoustic emission signals as an uncertainty measurement, to achieve selective frequency separation in complex impulsive-like noisy scenarios, with the aid of the spectral kurtosis as a validating tool. The goal consists of detecting relevant frequencies, by looking up the minima in the curve associated to the entropy of the difference between the raw data and the wavelet-based reconstructed version. By measuring the signal’s uncertainty, the scales corresponding to the entropy minima, or pseudo-frequencies, manage to target three main types of emissions generated by termites: the modulating components (enveloping curve), the carrier signals (activity, feeding and excavating), and the communicating impulses bursts (alarms). The spectral kurtosis corroborates the location of the entropy minima (optimum uncertainty) matching them to its maxima, associated to frequencies with the highest amplitude variability, and consequently minimizing the measurement uncertainty. The method is primarily conceived to cover the acoustic-range, in order to acquire signals via standard sound cards; a broaden high-frequency study is developed for the assessment, and with the added value of discovering new and higher frequency components of the species emissions. The potential of the method makes it useful for myriads of applications in the frame of nondestructive transient detection.     

CAN总线固定优先级可调度分析与设计方法及其在纯电动汽车中的应用

针对CAN总线固定优先级调度设计中存在的问题，在给出了消息模型和相关定义的基础上，首先推导了消息最糟糕响应时间，得出了系统可调度的判定定理；然后，提出了固定优先级设计方法FPSD；最后，以纯电动汽车为例进行了可调度分析和设计，验证了可调度判定定理和所提出的设计方法的有效性。