Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction.     

Automated Protein Subcellular Localization Based on Local Invariant Features

To understand the function of the encoded proteins, we need to be able to know the subcellular location of a protein. The most common method used for determining subcellular location is fluorescence microscopy which allows subcellular localizations to be imaged in high throughput. Image feature calculation has proven invaluable in the automated analysis of cellular images. This article proposes a novel method named LDPs for feature extraction based on invariant of translation and rotation from given images, the nature which is to count the local difference features of images, and the difference features are given by calculating the D-value between the gray value of the central pixel c and the gray values of eight pixels in the neighborhood. The novel method is tested on two image sets, the first set is which fluorescently tagged protein was endogenously expressed in 10 sebcellular locations, and the second set is which protein was transfected in 11 locations. A SVM was trained and tested for each image set and classification accuracies of 96.7 and 92.3 % were obtained on the endogenous and transfected sets respectively.     

Quantification of the Heterogeneity of Prognostic Cellular Biomarkers in Ewing Sarcoma Using Automated Image and Random Survival Forest Analysis

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 10⁴ features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour material could be achieved. Such an automated and integrated methodology has potential application in the identification of prognostic classifiers based on tumour cell heterogeneity.     

Application of LANDSAT MSS Digital Image Processing Techniques in Investigation of Aquatic Plant Distribution in Honghu Lake

In this paper an experiment in the application of LANDSAT MSS digital image proces. sing technique to classify the aquatic plants in the Honghu Lake, China, as well as the necessary ground feature spectrum measurement and plant chlorophyll content determination techniques are introduced. In order to obtain the expected computer aided classification result different methods of digital image feature extraction have been tried, namely the ratio transformation, the biomass index transformation and the linear stretching of image intensity. The data from spectrum measurement and chlorophyll content determination were used to distinguish the attributions of different classes of aquatic plant associations from one another by comparing with their spectrum response intensity values on different LANDSAT band images. The original LANDSAT MSS images were digitally rectified by control points to a certain map projection system using a general polynomial approach scheme before the classifying activities, so that the classification result may be transfered to an existing map. In this paper the resulting colourassigued image and the thematic map of Honghu Lakes aquatic plant (association) distribution are included, the areas of different classes of aquatic plants are listed and the reliability of the resulting classification and pattern recognition are analysed. These results will provide useful informations for the investigation of the present situation as well as the historical succession of the Honghu Lake and its aquatic plant distribution, and also for the research works of the lake management.     

Feature extraction from three-dimensional images in quantitative microscopy

Different methods are investigated in selecting and generating the appropriate microscope images for analysis of three-dimensional objects in quantitative microscopy. Traditionally, the ‘best’ focused image from a set is used for quantitative analysis. Such an objectively determined image is optimal for the extraction of some features, but may not be the best image for the extraction of all features. Various methods using multiple images are here developed to obtain a tighter distribution for all features.Three different approaches for analysis of images of stained cervical cells were analyzed. In the first approach, features are extracted from each image in the set. The feature values are then averaged to give the final result. In the second approach, a set of varying focused images are reconstructed to obtain a set of in-focus images. Features are then extracted from this set and averaged. In the third approach, a set of images in the three-dimensional scene is compressed into a single two-dimensional image. Four different compression methods are used. Features are then extracted from the resulting two-dimensional image. The third approach is employed on both the raw and transformed images.Each approach has its advantages and disadvantages. The first approach is fast and produces reasonable results. The second approach is more computationally expensive but produces the best results. The last approach overcomes the memory storage problem of the first two approaches since the set of images is compressed into one. The method of compression using the highest gradient pixel produces better results overall than other data reduction techniques and produces results comparable to the first approach.     

Effective Moment Feature Vectors for Protein Domain Structures

Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100–400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity.     

The rejection of noncellular artifacts in Papanicolaou-stained slide specimens by an automated high-resolution system. Identification of important cytometric features

The important cytodiagnostic features that permit discrimination of typical cell types by high-resolution image analysis and pattern recognition techniques have been previously studied in detail. An automated system for the diagnosis of Papanicolaou-stained specimens must also deal, however, with the "real world" of extraneous noncellular artifacts and debris found on every slide. Features that are ideal for the separation of typical normal and abnormal cells may not be adequate by themselves to reject these objects. A new set of discriminatory features must be found. In order to identify those features, a large set of images acquired using the TICAS high-resolution television rapid-scanning system was analyzed and studied. These images, from a variety of slide types, included normal cells, abnormal cells and noncellular artifacts identified by low-resolution preprocessing logic as suspicious enough to warrant high-resolution study. The results indicate that the more important features for such discrimination are not those traditionally important in distinguishing abnormal from normal cells but include color relations, shape measures, boundary properties and texture features.     

An algorithm for associating the features of two images.

In this paper we describe an algorithm that operates on the distances between features in the two related images and delivers a set of correspondences between them. The algorithm maximizes the inner product of two matrices, one of which is the desired 'pairing matrix' and the other a 'proximity matrix' with elements exp (-rij2/2 sigma 2), where rij is the distance between two features, one in each image, and sigma is an adjustable scale parameter. The output of the algorithm may be compared with the movements that people perceive when viewing two images in quick succession, and it is found that an increase in sigma affects the computed correspondences in much the same way as an increase in interstimulus interval alters the perceived displacements. Provided that sigma is not too small the algorithm will recover the feature mappings that result from image translation, expansion or shear deformation--transformations of common occurrence in image sequences--even when the displacements of individual features depart slightly from the general trend.     

Dual-Force ISOMAP: A New Relevance Feedback Method for Medical Image Retrieval

With great potential for assisting radiological image interpretation and decision making, content-based image retrieval in the medical domain has become a hot topic in recent years. Many methods to enhance the performance of content-based medical image retrieval have been proposed, among which the relevance feedback (RF) scheme is one of the most promising. Given user feedback information, RF algorithms interactively learn a user’s preferences to bridge the “semantic gap” between low-level computerized visual features and high-level human semantic perception and thus improve retrieval performance. However, most existing RF algorithms perform in the original high-dimensional feature space and ignore the manifold structure of the low-level visual features of images. In this paper, we propose a new method, termed dual-force ISOMAP (DFISOMAP), for content-based medical image retrieval. Under the assumption that medical images lie on a low-dimensional manifold embedded in a high-dimensional ambient space, DFISOMAP operates in the following three stages. First, the geometric structure of positive examples in the learned low-dimensional embedding is preserved according to the isometric feature mapping (ISOMAP) criterion. To precisely model the geometric structure, a reconstruction error constraint is also added. Second, the average distance between positive and negative examples is maximized to separate them; this margin maximization acts as a force that pushes negative examples far away from positive examples. Finally, the similarity propagation technique is utilized to provide negative examples with another force that will pull them back into the negative sample set. We evaluate the proposed method on a subset of the IRMA medical image dataset with a RF-based medical image retrieval framework. Experimental results show that DFISOMAP outperforms popular approaches for content-based medical image retrieval in terms of accuracy and stability.     

Scene-segmentation algorithm development using error measures

Development of scene-segmentation algorithms has generally been an ad hoc process. This paper presents a systematic technique for developing these algorithms using error-measure minimization. If scene segmentation is regarded as a problem of pixel classification whereby each pixel of a scene is assigned to a particular object class, development of a scene-segmentation algorithm becomes primarily a process of feature selection. In this study, four methods of feature selection were used to develop segmentation techniques for cervical cytology images: (1) random selection, (2) manual selection (best features in the subjective judgment of the investigator), (3) eigenvector selection (ranking features according to the largest contribution to each eigenvector of the feature covariance matrix) and (4) selection using the scene-segmentation error measure A2. Four features were selected by each method from a universe of 35 features consisting of gray level, color, texture and special pixel neighborhood features in 40 cervical cytology images . Evaluation of the results was done with a composite of the scene-segmentation error measure A2, which depends on the percentage of scenes with measurable error, the agreement of pixel class proportions, the agreement of number of objects for each pixel class and the distance of each misclassified pixel to the nearest pixel of the misclassified class. Results indicate that random and eigenvector feature selection were the poorest methods, manual feature selection somewhat better and error-measure feature selection best. The error-measure feature selection method provides a useful, systematic method of developing and evaluating scene-segmentation algorithms.     

Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth

The purpose of this study was to examine the dependence of image texture features on MR acquisition parameters and reconstruction using a digital MR imaging phantom. MR signal was simulated in a parallel imaging radiofrequency coil setting as well as a single element volume coil setting, with varying levels of acquisition noise, three acceleration factors, and four image reconstruction algorithms. Twenty-six texture features were measured on the simulated images, ground truth images, and clinical brain images. Subtle algorithm-dependent errors were observed on reconstructed phantom images, even in the absence of added noise. Sources of image error include Gibbs ringing at image edge gradients (tissue interfaces) and well-known artifacts due to high acceleration; two of the iterative reconstruction algorithms studied were able to mitigate these image errors. The difference of the texture features from ground truth, and their variance over reconstruction algorithm and parallel imaging acceleration factor, were compared to the clinical “effect size”, i.e., the feature difference between high- and low-grade tumors on T1- and T2-weighted brain MR images of twenty glioma patients. The measured feature error (difference from ground truth) was small for some features, but substantial for others. The feature variance due to reconstruction algorithm and acceleration factor were generally smaller than the clinical effect size. Certain texture features may be preserved by MR imaging, but adequate precautions need to be taken regarding their validity and reliability. We present a general simulation framework for assessing the robustness and accuracy of radiomic textural features under various MR acquisition/reconstruction scenarios.     

Classification of bronchial epithelial atypias by the atypia status index

The atypia status index (ASI) is a categorization method of classifying digitized images of atypical bronchial epithelial cells in sputum. The ASI is defined as a linear composite of features linearly related to atypia stage. Over 200 features were examined for more than 3,000 cells that had been classified by atypia stage (squamous metaplasia, mild, moderate or severe atypia and malignant) and staining characteristic (orangeophilia and nonorangeophilia). We reduced the number of features by using a selection process to minimize redundancy. The feature weights were optimized via a least-squares procedure. The 14 features selected accounted for over 60% of the variation of atypia stage and produced ASI values that were within one atypia stage of the criterion classification for over 90% of the cells. The results are consistent with the hypothesis of a progressive pattern in bronchial epithelial atypia and indicate the feasibility of using image analysis for mass screening of premalignant atypias in sputum from subjects considered to be at high risk for lung cancer.     

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction.     

基于灰度共生矩阵的人体皮肤纹理分析

对纹理图像的分析及特征提取是近年来图像处理领域的研究热点,在现实中有广泛的应用价值。灰度共生矩阵已被理论证明是图像纹理分析的一个很好的方法。为了使灰度共生矩阵所提取的特征值能够更好地描述皮肤老化的灰度分布信息,以不同年龄层的女性的不同部位的皮肤纹理图像作为研究对象,对采集到的图像进行预处理,采用灰度共生矩阵法提取纹理的特征值,通过统计分析得出了特征值的变化规律。实验结果对皮肤老化研究及其纹理分析有参考意义。     

使用图像特征构建快速有效的蛋白质折叠识别方法

蛋白质结构自动分类是探索蛋白质结构- 功能关系的一种重要研究手段。首先将蛋白质折叠子三维空间结构映射成为二维距离矩阵,并将距离矩阵视作灰度图像。然后基于灰度直方图和灰度共生矩阵提出了一种计算简单的折叠子结构特征提取方法,得到了低维且能够反映折叠结构特点的特征,并进一步阐明了直方图中零灰度孤峰形成原因,深入分析了共生矩阵特征中灰度分布、不同角度和像素距离对应的结构意义。最后应用于27类折叠子分类,对独立集测试的精度达到了71.95 %,对所有数据进行10 交叉验证的精度为78.94 %。与多个基于序列和结构的折叠识别方法的对比结果表明,此方法不仅具有低维和简洁的特征,而且无需复杂的分类系统,能够有效和高效地实现多类折叠子识别。     

A detection method for streak artifacts and radiological noise in a non-uniform region in a CT image

By using the CT images obtained by subtracting two CT images acquired under the same conditions and slice locations, we have devised a method for detecting streak artifacts in non-uniform regions and only radiological noise components in CT images. A chest phantom was scanned using 16- and 64-multidetector row helical CT scanners with various mAs values at 120 kVp. The upper lung slice image was employed as a target image for evaluating the streak artifacts and radiological noise. One hundred parallel line segments with a length of 80 pixels were placed on the subtracted CT image, and the largest CT value in each CT value profile was employed as a feature variable of the streak artifacts; these feature variables were analyzed with the extreme value theory (Gumbel distribution). To detect only the radiological noise, all CT values contained in the 100 line profile were plotted on normal probability paper and the standard deviation was estimated from the inclination of its fitted line for the CT value plots. The two detection methods devised in this study were able to evaluate the streak artifacts and radiological noise in the CT images with high accuracy.     

Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Background

Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein subcellular location patterns in both 2D and 3D fluorescence microscope images. Building on these results, we evaluate here new classifiers and features to improve the recognition of protein subcellular location patterns in both 2D and 3D fluorescence microscope images.

Results

We report here a thorough comparison of the performance on this problem of eight different state-of-the-art classification methods, including neural networks, support vector machines with linear, polynomial, radial basis, and exponential radial basis kernel functions, and ensemble methods such as AdaBoost, Bagging, and Mixtures-of-Experts. Ten-fold cross validation was used to evaluate each classifier with various parameters on different Subcellular Location Feature sets representing both 2D and 3D fluorescence microscope images, including new feature sets incorporating features derived from Gabor and Daubechies wavelet transforms. After optimal parameters were chosen for each of the eight classifiers, optimal majority-voting ensemble classifiers were formed for each feature set. Comparison of results for each image for all eight classifiers permits estimation of the lower bound classification error rate for each subcellular pattern, which we interpret to reflect the fraction of cells whose patterns are distorted by mitosis, cell death or acquisition errors. Overall, we obtained statistically significant improvements in classification accuracy over the best previously published results, with the overall error rate being reduced by one-third to one-half and with the average accuracy for single 2D images being higher than 90% for the first time. In particular, the classification accuracy for the easily confused endomembrane compartments (endoplasmic reticulum, Golgi, endosomes, lysosomes) was improved by 5–15%. We achieved further improvements when classification was conducted on image sets rather than on individual cell images.

Conclusions

The availability of accurate, fast, automated classification systems for protein location patterns in conjunction with high throughput fluorescence microscope imaging techniques enables a new subfield of proteomics, location proteomics. The accuracy and sensitivity of this approach represents an important alternative to low-resolution assignments by curation or sequence-based prediction.

     

Stereo super-resolution images detection based on multi-scale feature extraction and hierarchical feature fusion

Recently, with most mobile phones coming with dual cameras, stereo image super-resolution is becoming increasingly popular in phones and other modern acquisition devices, leading stereo super-resolution images spread widely on the Internet. However, current image forensics methods are carried out in monocular images, and high false positive rate appears when detecting stereo super-resolution images by these methods. Therefore, it is important to develop stereo super-resolution image detection method. In this paper, a convolutional neural network with multi-scale feature extraction and hierarchical feature fusion is proposed to detect the stereo super-resolution images. Multi-atrous convolutions are employed to extract multi-scale features and be adapt for varying stereo super-resolution images, and hierarchical feature fusion further improve the performance and robustness of the model. Experimental results demonstrate that the proposed network can detect stereo super-resolution images effectively and achieve strong generalization and robustness. To the best of our knowledge, it is the first attempt to investigate the performance of current forensics methods when tested under stereo super-resolution images, and represent the first study of stereo super-resolution images detection. We believe that it can raise the awareness about the security of stereo super-resolution images.