Pattern Recognition Software and Techniques for Biological Image Analysis

In recent years the Comet assay (or single cell gel electrophoresis assay) has been established as a rapid and sensitive method for the detection of DNA damage. For early genotoxicity screening of new chemical entities in industrial toxicology, the Comet assay is more and more used for assessment of the DNA damaging potential of a test compound. In order to increase compound screening throughput, we have established an image analysis system for fully automated measurement of microscope slides processed in the Comet assay. For the comparative investigation various cell types, such as V79 Chinese hamster cells, mouse lymphoma cells and human leukocytes, were treated with several test compounds. Using tail moment as the quantitative parameter for comet formation, we show a very high correlation between our automatic image analysis system and a commercially available, interactive system (Comet Assay II of Perceptive Instruments). The possibility of analyzing 50 samples within 1 day and the high reproducibility of results make automated image processing a powerful tool for automatic analysis of slides processed in the Comet assay.

A Novel Automated Image Analysis System Using Deep Convolutional Neural Networks to Diagnose MDS

Age-related changes in vertebral cortical and trabecular bone were quantitated in female CW-1 mice. Histomorphometric measurements involved the use of two different systems of image analysis: Olympus Cue 2 and Zeiss Morphomat 10. The peak of bone mass, both cortical and trabecular, was found in mice aged 13.5 months. Thereafter, there was a progressive decline in the area occupied by bone tissue and this became highly significant in elderly female and male mice. A loss of about 60% of bone tissue was encountered in both the trabecular and cortical bone, as determined by automated image analysis system. Hence, aging CW-1 mice reveal structural features consistent with vertebral osteopenia. Despite the fact that to date we lack the precise etiology for the above-mentioned phenomenon, it is suggested that the laboratory mouse might serve as an appropriate experimental model for investigations related to age-related bone loss.

Application of automated image analysis to coal petrography

Somatic embryogenesis is among the most promising means of large scale plant micropropagation. The development of somatic embryos is characterized by their morphological changes. Embryos in culture usually exhibit high heterogeneity and abnormality. As conventional microscopic observation is laborious and subjective, an objective and quantitative morphokinetic description is important for further advancement of this important process technology. We developed an image analysis system capable of measuring morphological and size features of embryos. Subtle environmental effects on embryo development, which are often masked by the subjectivity of microscopic observation, are now discernible by statistically comparing the distributions of these morphological and size features. This image analysis and pattern recognition system was applied to examine the kinetics of a fed-batch culture.

Pattern recognition techniques can potentially be used to quantitatively analyze a wide variety of biomedical images. A challenge in applying this methodology is that biomedical imaging uses many imaging modalities and subjects. Pattern recognition relies on numerical image descriptors (features) to describe image content. Thus, the application of pattern recognition to biomedical imaging requires the development of a wide variety of image features. In this study we compared the efficacy of different techniques for constructing large feature spaces. A two-stage method was employed where several types of derived images were used as inputs for a bank of feature extraction algorithms. Image pyramids, subband filters, and image transforms were used in the first-stage. The feature bank consisted of polynomial coefficients, textures, histograms and statistics as previously described [1]. The basis for comparing the performance of these feature sets was the biological imaging benchmark described in [2]. Our results show that a set of image transforms (Fourier, Wavelet, Chebyshev) performed significantly better than a set of image filters (image pyramids, sub-band filters, and spectral decompositions). The transform technique was used to analyze images of H&E-stained tissue biopsies from two cancers: lymphoma (three types of malignancies) and melanoma (benign, primary, and five secondary tumor sites). The overall classification accuracy for these cancer data sets was 97%.

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values ( Sustainable Analysis ). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration ( Robust Analysis ). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters. In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

For real-time systems, most of the analysis involves efficient or exact schedulability checking. While this is important, analysis is often based on the assumption that the task parameters such as execution requirements and inter-arrival times between jobs are known exactly. In most cases, however, only a worst-case estimate of these quantities is available at the time of analysis. It is therefore imperative that schedulability analysis hold for better parameter values (Sustainable Analysis). On the other hand, if the task or system parameters turn out to be worse off, then the analysis should tolerate some deterioration (Robust Analysis). Robust analysis is especially important, because the implication of task schedulability is often weakened in the presence of optimizations that are performed on its code, or dynamic system parameters.In this work, we define and address sustainability and robustness questions for analysis of embedded real-time software that is modeled by conditional real-time tasks. Specifically, we show that, while the analysis is sustainable for changes in the task such as lower job execution times and increased relative deadlines, it is not the case for code changes such as job splitting and reordering. We discuss the impact of these results in the context of common compiler optimizations, and then develop robust schedulability techniques for operations where the original analysis is not sustainable.

Image segmentation is the first step in image analysis and pattern recognition, and it is one of the most difficult tasks in image processing, and determines the quality of the final result of analysis. Image segmentation requires the improvement because most image segmentation solution is problem based. Biomedical image segmentation is different than normal image segmentation. Because normal image is taken from camera which is free of noise. While biomedical image is taken from MRI, CT, Microscopic Instrument or X-ray which is it noisy and fuzzy. Medical image segmentation methods generally have restrictions because medical images have very similar gray level and texture among the interested. While extracting region of interest in biomedical image, region of interest can avoid the processing of irrelevant image point. Biomedical Image segmentation is necessary when we want the computer to make decision, surgical planning and robotically assisted surgical invention. Biomedical image segmentation is nothing but the classification of similar pixel and their following extraction into separate segment. There are different methods to do the Biomedical image segmentation but they are slow. he method which is described in this paper is fast and efficient. This paper gives comparative research of different method of each of category. The method described in the paper is for fast biomedical image segmentation

The shape features of objects (connected components) in binary images are very important for image analysis, pattern (object) recognition, and computer vision. Conventional algorithms for calculating the shape features of objects in binary images can only calculate the shape features of objects without holes. This paper presents an algorithm or calculating the shape features of objects in binary images with holes. Based on a contour-tracing-based connected-component labeling algorithm, our proposed algorithm can calculate the number of objects, the number of holes, the Euler number in a binary image, extract the contours of objects, and calculate the area, perimeter, circularity, centroid of each object. Our proposed algorithm is able to make a contribution to image analysis, pattern recognition, and computer vision.

Bio-Inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition comprises papers on diverse aspects of bio-inspired models, soft computing and hybrid intelligent systems. The articles are divided into four main parts. The first one consists of papers that propose new fuzzy and bio-inspired models to solve general problems. The second part deals with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques. The third part contains papers that apply hybrid intelligent systems to the problem of time series analysis and prediction, while the fourth one shows papers dealing with bio-inspired models in optimization and robotics applications. An edited book in which both theoretical and application aspects are covered.

Foreword. Correcting the Training Data R. Barandela, et al. Context Free Grammars and Semantic Networks for Flexible Assembly Recognition C. Bauckhage, G. Sagerer. Stochastic Recognition of Occluded Objects B. Bhanu, et al. Approximate String Matching for Angular String Elements with Applications to On-Line and Off-line Handwriting Recognition S.-H. Cha, S.N. Srihari. Uniform, Fast Convergence of Arbitrarily Tight Upper and Lower Bounds on the Bayes Error D. Chen, et al. Building RBF Networks for Time Series Classification by Boosting J.R. Diez, C.J.A. Gonzalez. Similarity Measures and Clustering of String Patterns A. Fred. Pattern Recognition for Intrusion Detection in Computer Networks G. Giacinto, F. Roli. Model-Based Pattern Recognition M. Haindl. Structural Pattern Recognition in Graphs L. Holder, et al. Deriving Pseudo-Probabilities of Correctness Given Scores (DPPS) K. Ianakiev, V. Govindaraju. Weighed Mean and Generalized Median of Strings Y. Jiang, H. Bunke. A Region-Based Algorithm for Classifier-Independent Feature Selection M. Kudo. Inference of K-Piecewise Testable Tree Languages D. Lopez, et al. Mining Partially Periodic Patterns With Unknown Periods From Event tream S. Ma, J.L. Hellerstein. Combination of Classifiers for Supervised Learning: A Survey S. Ma, C. Ji. Image Segmentation and Pattern Recognition: A Novel Concept, the Historgram of Connected Elements D. Maravell, M.A. Patricio. Prototype Extraction for k-NN Classifiers using Median Srings C.D. Martinez-Hinarejos, et al. Cyclic String Matching: Efficient Exact and Approximate Algorithms A. Marzal, et al. Homogeneity, Autocorrelation and Anisotropy in Patterns A. Molina. Robust Structural Indexing through Quasi-Invariant Shape Signatures and FeatureGeneration H. Nishida. Energy Minimisation Methods for Static and Dynamic Curve Matching E. Nyssen, et al. Recent Feature Selection Methods in Statistical Pattern Recognition P. Pudil, et al. Fast Image Segmentation under Noise R.M. Romano, D. Vitulano. Set Analysis of Coincident Errors and Its Applications for Combining Classifiers D. Ruta, B. Gabrys. Enhanced Neighbourhood Specifications for Pattern Classification J.S. San nchez, A.I. Marques. Algorithmic Synthesis in Neural Network Training for Pattern Recognition K. Sirlantzis. Binary Strings and multi-class learning problems T. Windeatt, R. Ghaderi.

automatic quantitative image analysis system for con- struction materials.

Moving target detection and recognition methods are the foundation and key of modern intelligent video recognition systems. It combines advanced technologies in many fields such as image processing, pattern recognition, and artificial intelligence, and is a research hotspot in computer vision technology. Therefore, it is of great significance to study moving target detection and recognition algorithms. The non-local image extraction algorithm proposed in this paper uses an adaptive clustering method to perform fine cluster analysis on non-local image blocks with different feature types. Through the step-by-step principal component approximation method, we carefully find the features in each class. This progressive principal component approximation implements singular value hard threshold processing based on the Marchenko-Pastur (MP) theorem to select the main part of the feature, and uses special soft thresholds in the principal component transform domain to further improve the extraction performance. The Lower Bound-Based Within-Class Maximum Division (LBWCMD) is proposed, and this method is used as a preprocessing step of robust principal component analysis in moving target detection. This article applies LBWCMD to the video frame set based on the position information of the moving target, and the obtained frame subset meets the signal requirements of Robust Principle Component Analysis (RPCA) to the greatest extent. On this basis, we add frames with smaller motion amplitudes to each frame subset to increase the proportion of background pixels in each subset. Frame set division and low-rank decomposition realize the detection of moving targets under a unified framework. The detection rate of the proposed method is higher than that of the current popular methods in sports video data sets, and the detection accuracy is improved compared with the original RPCA method.

Patterns and functioning of communities, which are determined by a set of processes operating at a large variety of spatial and temporal scales, exhibit quite high context‐dependency and low predictability at the fine spatial scales at which recent studies have concentrated. More attention to broader scale and across‐scale phenomena may be useful to search for general patterns and rules in communities. In this context, it is effective to incorporate hierarchical spatial scale explicitly into the experimental and sampling design of field studies, an approach referred to here as the spatial hierarchical approach, focusing on a particular assemblage in which biological interaction and species life history are well known. The spatial hierarchical approach can provide insight into the effects of scale in operating processes and answers to a number of important questions in community ecology such as: (1) detection of patterns and processes in spatiotemporal variability in communities, including how to explain the partitioning of pattern information of species diversity at a broad scale into finer scales, and the pattern of spatial variability of community properties at the finest spatial scale; (2) evaluation of changes in patterns observed in macroecology at finer scales; (3) testing of models explaining the coexistence of competing species; and (4) detection of latitudinal patterns in spatiotemporal variability in communities and their causal processes.