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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2011年 7036 LNBI卷 V-VI页

作者： Loog, Marco Wessels, Lodewyk Reinders, Marcel De Ridder, Dick Delft University of Technology Pattern Recognition and Bioinformatics Group Mekelweg 4 2628 CD Delft Netherlands Netherlands Cancer Institute Bioinformatics and Statistics Plesmanlaan 121 1066 CX Amsterdam Netherlands

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Epithelial Area Detection in Cytokeratin Microscopic Images Using MSER Segmentation in an Anisotropic Pyramid

Epithelial Area Detection in Cytokeratin Microscopic Images ...

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6th iapr international conference on pattern recognition in bioinformatics

作者： Smochina, Cristian Rogojanu, Radu Manta, Vasile Kropatsch, Walter Gheorghe Asachi Tech Univ Iasi Fac Automat Control & Comp Engn Iasi Romania Med Univ Inst Pathophysiol & Allergy Res Vienna Austria Vienna Univ Technol Inst Comp Graph & Algorithms Pattern Recognit & Image Proc Grp A-1040 Vienna Austria

ISBN: (纸本)9783642248542

the objective of semantic segmentation in microscopic images is to extract the cellular, nuclear or tissue components. this problem is challenging due to the large variations of features of these components (size, shape, orientation or texture). In this paper we present an automatic technique to robustly delimit the epithelial area (crypts) in microscopic images taken from colon tissues sections marked with cytokeratin-8. the epithelial area is highlighted using the anisotropic diffusion pyramid and segmented using MSER+. the crypts separation and lumen detection is performed by imposing topological constraints about the epithelial layer distribution within the tissue and the round-like shape of the crypt. the evaluation of the proposed method is made by comparing the results with ground-truth segmentations.

关键词： Crypt segmentation Anisotropic diffusion pyramid MSER Biomedical imaging Pathology Microscopy Fluorescence microscopy Image analysis

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pattern recognition in High-Content Cytomics Screens for Target Discovery - Case Studies in Endocytosis

Pattern Recognition in High-Content Cytomics Screens for Tar...

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6th iapr international conference on pattern recognition in bioinformatics

作者： Cao, Lu Yan, Kuan Winkel, Leah de Graauw, Mado Verbeek, Fons J. Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands Leiden Univ Toxicol Leiden Amsterdam Ctr Drug Res Leiden Netherlands

ISBN: (纸本)9783642248542

Finding patterns in time series of images requires dedicated approaches for the analysis, in the setup of the experiment, the image analysis as well as in the pattern recognition. the large volume of images that are used in the analysis necessitates an automated setup. In this paper, we illustrate the design and implementation of such a system for automated analysis from which phenotype measurements can be extracted for each object in the analysis. Using these measurements, objects are characterized into phenotypic groups through classification while each phenotypic group is analyzed individually. the strategy that is developed for the analysis of time series is illustrated by a case study on EGFR endocytosis. Endocytosis is regarded as a mechanism of attenuating epidermal growth factor receptor (EGFR) signaling and of receptor degradation. Increasingly, evidence becomes available showing that cancer progression is associated with a defect in EGFR endocytosis. Functional genomics technologies combine high-throughput RNA interference with automated fluorescence microscopy imaging and multi-parametric image analysis, thereby enabling detailed insight into complex biological processes, like EGFR endocytosis. the experiments produce over half a million images and analysis is performed by automated procedures. the experimental results show that our analysis setup for high-throughput screens provides scalability and robustness in the temporal analysis of an EGFR endocytosis model.

关键词： phenotype measurement image analysis classification EGFR endocytosis

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Application of the Multi-modal Relevance Vector Machine to the Problem of Protein Secondary Structure Prediction

Application of the Multi-modal Relevance Vector Machine to t...

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7th international-Association-for-pattern-recognition (iapr) international conference on pattern recognition in bioinformatics (prib)

作者： Razin, Nikolay Sungurov, Dmitry Mottl, Vadim Torshin, Ivan Sulimova, Valentina Seredin, Oleg Windridge, David Moscow Inst Phys & Technol Moscow Russia Russian Acad Sci Ctr Comp Moscow Russia Tula State Univ Tula Russia Univ Surrey Guildford Surrey England

ISBN: (纸本)9783642341236

the aim of the paper is to experimentally examine the plausibility of Relevance Vector Machines (RVM) for protein secondary structure prediction. We restrict our attention to detecting strands which represent an especially problematic element of the secondary structure. the commonly adopted local principle of secondary structure prediction is applied, which implies comparison of a sliding window in the given polypeptide chain with a number of reference amino-acid sequences cut out of the training proteins as benchmarks representing the classes of secondary structure. As distinct from the classical RVM, the novel version applied in this paper allows for selective combination of several tentative window comparison modalities. Experiments on the RS126 data set have shown its ability to essentially decrease the number of reference fragments in the resulting decision rule and to select a subset of the most appropriate comparison modalities within the given set of the tentative ones.

关键词： Protein secondary structure prediction machine learning multi-modal relational pattern recognition Relevance Vector Machine controlled selectivity of reference objects and object-comparison modalities

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

ISBN: (纸本)9783319116556

the proceedings contain 27 papers. the special focus in this conference is on Artificial Neural Networks for pattern recognition. the topics include: Large margin distribution learning;a decorrelation approach for pruning of multilayer perceptron networks;unsupervised active learning of CRF model for cross-lingual named entity recognition;incremental feature selection by block addition and block deletion using least squares SVRs;low-dimensional data representation in data analysis;analyzing dynamic ensemble selection techniques using dissimilarity analysis;hidden Markov models based on generalized Dirichlet mixtures for proportional data modeling;majority-class aware support vector domain oversampling for imbalanced classification problems;forward and backward forecasting ensembles for the estimation of time series missing data;dynamic weighted fusion of adaptive classifier ensembles based on changing data streams;combining bipartite graph matching and beam search for graph edit distance approximation;computing upper and lower bounds of graph edit distance in cubic time;a new multi-class fuzzy support vector machine algorithm;a reinforcement learning algorithm to train a Tetris playing agent;automatic bridge crack detection - a texture analysis-based approach;part-based high accuracy recognition of serial numbers in bank notes;comparative study of feature selection for white blood cell differential counts in low resolution images;end-shape recognition for Arabic handwritten text segmentation;intelligent ensemble systems for modeling NASDAQ microstructure;face recognition based on discriminative dictionary with multilevel feature fusion;investigating of preprocessing techniques and novel features in recognition of handwritten Arabic characters and a time series classification approach for motion analysis using ensembles in ubiquitous healthcare systems.

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Multi-scale Block Local Ternary patterns for Fingerprints Vitality Detection

Multi-scale Block Local Ternary Patterns for Fingerprints Vi...

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6th iapr international conference on Biometrics (ICB)

作者： Jia, Xiaofei Yang, Xin Zang, Yali Zhang, Ning Dai, Ruwei Tian, Jie Zhao, Jianmin Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Zhejiang Normal Univ Coll Math Phys & Informat Technol Zhejiang Peoples R China

ISBN: (纸本)9781479903108

Fingerprint recognition systems are widely deployed in both government and civilian applications. But the emergence of fake fingerprints poses a new threat to privacy security. Among the numerous fingerprint vitality detection methods, the local binary pattern (LBP) is considered as one of the state-of-the-art operators. However, the local binary pattern tends to be sensitive to noise, as there are no types of filters involved in the LBP operator. Worse still, the LBP operator can not reflect these difference whether the pixel value is bigger than the threshold or equal to the threshold. So we proposed a novel fingerprint vitality detection method based on multi-scale block local ternary patterns (MBLTP). Instead of a single pixel, its computation is done based on the average value of blocks. the ternary pattern is adopted to reflect the differences between the pixels and the threshold. the experimental results in the databases of Competition on Fingerprint Liveness Detection 2011 (LivDet 2011) show its superiority.

关键词： Local binary pattern

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5th international Workshop on Computational Forensics, IWCF 2012 and 6th international Workshop on Computational Forensics, IWCF 2014

5th International Workshop on Computational Forensics, IWCF ...

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5th international Workshop on Computational Forensics, IWCF 2012 and 6th international Workshop on Computational Forensics, IWCF 2014

ISBN: (纸本)9783319201245

the proceedings contain 20 papers. the special focus in this conference is on Biometrics, Document Image Inspection and Applications. the topics include: Voice passphrase variability evaluation for speaker recognition;studies in individuality;robust 2d face recognition under different illuminations using binarized partial face features;comparison of multidirectional representations for multispectral palmprint recognition;efficient iris recognition system using relational measures;a study of identification performance of facial regions from CCTV images;inverse of low resolution line halftone images for document inspection;when document security brings new challenges to document analysis;stamp verification for automated document authentication;introducing and analysis of the windows 8 event log for forensic purposes;automatic creation of computer forensic test images;art forgery detection via craquelure pattern matching;forensics acquisition and analysis of instant messaging and VOIP applications and an integrated tool for forensic writer identification.

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A novel hybrid CNN-AIS visual pattern recognition engine 6th

A novel hybrid CNN-AIS visual pattern recognition engine

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6th international conference on pattern recognition and Machine Intelligence, PReMI 2015

作者： Bhalla, Vandna Chaudhury, Santanu Jain, Arihant Indian Institute of Technology Delhi New Delhi India

Machine learning methods are used today mostly for recognition problems. Convolutional Neural Networks (CNN) have time and again proved successful for many image processing tasks primarily for their architecture. In this paper we propose to apply CNN to small data sets like for example, personal photo albums or other similar environs where the size of training dataset is a limitation, within the framework of a proposed hybrid CNN-AIS model. We use Artificial Immune System Principles to enhance the small size of training data set. A layer of Clonal Selection is added to the local filtering and max pooling of CNN Architecture. the proposed Architecture is evaluated using the standard MNIST dataset by limiting the data size and also with a small personal data sample belonging to two different classes. Experimental results show that the proposed hybrid CNN-AIS based recognition engine works well when the size of training data is limited in size. © Springer international Publishing Switzerland 2015.

关键词： Neural networks

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Metabolic pathway inference from time series data: A non iterative approach

Metabolic pathway inference from time series data: A non ite...

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6th iapr international conference on pattern recognition in bioinformatics, prib 2011

作者： Astola, Laura Groenenboom, Marian Gomez Roldan, Victoria Van Eeuwijk, Fred Hall, Robert D. Bovy, Arnaud Molenaar, Jaap Biometris Wageningen University and Research Centre Droevendaalsesteeg 1 Wageningen Netherlands Netherlands Consortium for Systems Biology Amsterdam Netherlands Plant Research International Wageningen University Research Centre Wageningen Netherlands Centre for Biosystems Genomics P.O. Box 98 Wageningen Netherlands

ISBN: (纸本)9783642248542

In this article, we present a very fast and easy to implement method for reconstruction of metabolic pathways based on time series data. To model the metabolic reactions, we use the well-established setting of ordinary differential equations. In the present article we consider a network leading to the accumulation of quercetin-glycosides in tomato (Solanum lycopersicum). Quercetin belongs to a group of plant secondary metabolites, generally referred to as flavonoids, which are extensively being studied for their variety of important functions in plants as well as for their potentially health-promoting effects on human. We use time series measurements of metabolite concentrations of quercetin derivatives. In the present setting, the observed concentrations are the variables and the reaction rates are the unknown parameters. A standard method is to solve the parameters by reverse engineering, where the ordinary differential equations (ODE) are solved repeatedly, resulting in impractical computation times. We use an alternative method that estimates the parameters by least squares minimization, and which is, in the order of hundred times faster than the iterative method. Our reconstruction method can incorporate an arbitrary a priori known network structure as well as positivity constraints on the reaction rates. In this way we can avoid over-fitting, which is another often encountered problem in network reconstruction, and thus obtain better estimates for the parameters. We test the presented method by reconstructing artificial networks and compare it with the more conventional method in terms of residuals between the observed and fitted concentrations, computing times and the proportion of correctly identified edges in the network. Finally we exploit this fast method to statistically infer the kinetic constants in the flavonoid pathway. We remark that the method as such is not limited to metabolic network reconstructions, but can be used with any type of time-series

关键词： Ordinary differential equations

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Image and Signal Processing - 6th international conference, ICISP 2014, Proceedings

Image and Signal Processing - 6th International Conference, ...

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6th international conference on Image and Signal Processing, ICISP 2014

ISBN: (纸本)9783319079974

the proceedings contain 76 papers. the topics discussed include: daylight colored optimal spectra for improved color discrimination;evaluating visibility of age spot and freckle based on simulated spectral reflectance of skin;multiple color matches to estimate human color vision sensitivities;building a two-way hyperspectral imaging system with liquid crystal tunable filters;measurement and modeling of bidirectional characteristics of fluorescent objects;multispectral endoscopy to identify precancerous lesions in gastric mucosa;multichannel spectral image enhancement for visualizing diabetic retinopathy lesions;fundamental study on intraoperative quantification of gastrointestinal viability by transmission light intensity analysis;and improved spectral density measurement from estimated reflectance data with kernel ridge regression.

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