Advanced Image Acquisition, Processing Techniques and Applications

"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution

ATMAD : robust image analysis for Automatic Tissue MicroArray De-arraying

International audienceBackground. Over the last two decades, an innovative technology called Tissue Microarray (TMA),which combines multi-tissue and DNA microarray concepts, has been widely used in the field ofhistology. It consists of a collection of several (up to 1000 or more) tissue samples that are assembledonto a single support – typically a glass slide – according to a design grid (array) layout, in order toallow multiplex analysis by treating numerous samples under identical and standardized conditions.However, during the TMA manufacturing process, the sample positions can be highly distorted fromthe design grid due to the imprecision when assembling tissue samples and the deformation of theembedding waxes. Consequently, these distortions may lead to severe errors of (histological) assayresults when the sample identities are mismatched between the design and its manufactured output.The development of a robust method for de-arraying TMA, which localizes and matches TMAsamples with their design grid, is therefore crucial to overcome the bottleneck of this prominenttechnology.Results. In this paper, we propose an Automatic, fast and robust TMA De-arraying (ATMAD)approach dedicated to images acquired with bright field and fluorescence microscopes (or scanners).First, tissue samples are localized in the large image by applying a locally adaptive thresholdingon the isotropic wavelet transform of the input TMA image. To reduce false detections, a parametricshape model is considered for segmenting ellipse-shaped objects at each detected position.Segmented objects that do not meet the size and the roundness criteria are discarded from thelist of tissue samples before being matched with the design grid. Sample matching is performed byestimating the TMA grid deformation under the thin-plate model. Finally, thanks to the estimateddeformation, the true tissue samples that were preliminary rejected in the early image processingstep are recognized by running a second segmentation step.Conclusions. We developed a novel de-arraying approach for TMA analysis. By combining waveletbaseddetection, active contour segmentation, and thin-plate spline interpolation, our approach isable to handle TMA images with high dynamic, poor signal-to-noise ratio, complex background andnon-linear deformation of TMA grid. In addition, the deformation estimation produces quantitativeinformation to asset the manufacturing quality of TMAs

Detecting influential transcription factors using linear models

Transkriptsioonifaktorite tuvastamine on aktuaalne probleem molekulaarbioloogias. Tänapäeval võimaldavad erinevad tehnoloogilised saavutused jälgida rakus toimuvaid protsesse, kuigi nende analüüs ei ole triviaalne ülesanne, mis vajab erinevate teaduste kaasamist. Töös kirjeldatakse lineaarsete mudelite kasutamise võimalusi oluliste transkriptsioonifaktorite tuvastamiseks mikrokiibi andmetest. Lineaarse mudeli parameetreid võib käsitleda kui transkriptsioonifaktorite olulisust määravaid näitajaid. Töös on vaadeldud erinevad lineaarregressiooni meetodid koos nende iseärasuste põhjaliku kirjeldusega ning on analüüsitud nende sobivus bioloogiliseks rakenduseks.With the recent development of the high throughput DNA microarray technology, it became possible to measure the levels of gene activity on a large scale. The data collected from a microarray usually requires sophisticated analysis involving biological knowledge and the application of statistical techniques. In this work the problem of inferring ‘influential’ transcription factors from microarray data using linear models is addressed. Linear models are easy to understand and are able to produce interpretable solutions. The state-of-the-art methods for solving linear regression problems and their applicability to biological data are described in the paper

The Design and Construction of Novel Near -Infrared Time -Correlated Single Photon Counting Devices for the Identification of Analytes in Multiplexed Applications.

This manuscript details the design, construction, and application of novel near infrared time correlated single photon counting devices to the identification of analytes in analytical separations. The thrust of this research is to provide a simple, low cost technique for the high-speed identification of DNA sequencing bases that are labeled with a series of unique near infrared fluorophores. These fluorophores are unique because they possess the same emission and absorption maxima, but different fluorescence lifetimes. Consequently, they allow analytes to be discriminated by fluorescence lifetime as opposed to color. The first goal of this dissertation research was to implement a time correlated single photon counting system with the use of single mode fiber optics. Utilizing a passively mode locked Ti: Sapphire Laser, a single photon avalanche diode, single mode fiber optics and a mechanical switch a fiber optic based time correlated single photon counting device with subnanosecond resolution was constructed. The experimental results showed that group velocity dispersion was low and that it was possible to perform multiple time correlated single photon counting experiments with a limited number of excitation sources and detectors. It was determined that the average instrumental response of each channel was 181 picoseconds. The fluorescence lifetime of a near infrared dye, aluminum tetrasulfonated naphthalocyanine was determined to be 3.08 nanoseconds. The second phase of this doctoral research involved the construction and characterization of a near infrared time correlated single photon counting scanning device. This integrated device consisted of a pulsed diode laser, single photon avalanche diode, and a time correlated single photon counting board. The instrument response function of this system was determined to be less than 300 ps. The sensitivity and ability to discriminate between various fluorophores was determined. In addition to its application for scanning solid surfaces such as DNA microarrays, the device was utilized to detect analytes in a micro-capillary electrophoresis separation. The fluorescence lifetimes of these analytes were determined on-line

Integrating snp data and imputation methods into the DNA methylation analysis framework

DNA methylation is a widely studied epigenetic modification that can influence the expression and regulation of functional genes, especially those related to aging, cancer and other diseases. The common goal of methylation studies is to find differences in methylation levels between samples collected under different conditions. Differences can be detected at the site level, but regulated methylation targets are most commonly clustered into short regions. Thus, identifying differentially methylated regions (DMRs) between different groups is of prime interest. Despite advanced technology that enables measuring methylation genome-wide, misinterpretations in the readings can arise due to the existence of single nucleotide polymorphisms (SNPs) in the target sequence. One of the main pre-processing steps in DMR detection methods involves filtering out potential SNP-related probes due to this issue. In this work, it is proposed to leverage the current trend of collecting both SNP and methylation data on the same individual, making it possible to integrate SNP data into the DNA methylation analysis framework. This will enable the originally filtered potential SNPs to be restored if a SNP is not actually present. Furthermore, when a SNP is present or other missing data issues arise, imputation methods are proposed for methylation data. First, regularized linear regression (ridge, LASSO and elastic net) imputation models are proposed, along with a variable screening technique to restrict the number of variables in the models. Functional principal component regression imputation is also proposed as an alternative approach. The proposed imputation methods are compared to existing methods and evaluated based on imputation accuracy and DMR detection ability using both real and simulated data. One of the proposed methods (elastic net with variable screening) shows effective imputation accuracy without sacrificing computation efficiency across a variety of settings, while greatly improving the number of true positive DMR detections --Abstract, page iii

Chemometric methods for microarray data analysis and their application to leukemia subtype identification

Verschiedene chemometrische Methoden wurden entwickelt, die die komplette Datenverarbeitungskette bei der Analyse von Affymetrix U133 DNA Biosensoren umfassen. Ziel war es die Qualität der Daten zu erhöhen. Dafür wurden Indikatoren erstellt, mit deren Hilfe es möglich ist, Signale mangelnder Qualität zu detektieren, sowie Hintergrund und Artefakte zu entfernen. Diese Methoden können mit einem ebenfalls neu entwickeltes Datenbank-System verwendet werden, um bei der gesamten Datenverarbeitung die Qualität der Daten zu gewährleisten. Angewandt wurde dieses System bei der Diskriminierung von verschiedenen pädiatrischen Leukämie-Typen. Es wurden Indikator-Gene gefunden, mit deren Hilfe unbekannte Leukämie-Proben klassifiziert werden können

Sparse graphical models for cancer signalling

Protein signalling networks play a key role in cellular function, and their dysregulation is central to many diseases, including cancer. Recent advances in biochemical technology have begun to allow high-throughput, data-driven studies of signalling. In this thesis, we investigate multivariate statistical methods, rooted in sparse graphical models, aimed at probing questions in cancer signalling. First, we propose a Bayesian variable selection method for identifying subsets of proteins that jointly in uence an output of interest, such as drug response. Ancillary biological information is incorporated into inference using informative prior distributions. Prior information is selected and weighted in an automated manner using an empirical Bayes formulation. We present examples of informative pathway and network-based priors, and illustrate the proposed method on both synthetic and drug response data. Second, we use dynamic Bayesian networks to perform structure learning of context-specific signalling network topology from proteomic time-course data. We exploit a connection between variable selection and network structure learning to efficiently carry out exact inference. Existing biology is incorporated using informative network priors, weighted automatically by an empirical Bayes approach. The overall approach is computationally efficient and essentially free of user-set parameters. We show results from an empirical investigation, comparing the approach to several existing methods, and from an application to breast cancer cell line data. Hypotheses are generated regarding novel signalling links, some of which are validated by independent experiments. Third, we describe a network-based clustering approach for the discovery of cancer subtypes that differ in terms of subtype-specific signalling network structure. Model-based clustering is combined with penalised likelihood estimation of undirected graphical models to allow simultaneous learning of cluster assignments and cluster-specific network structure. Results are shown from an empirical investigation comparing several penalisation regimes, and an application to breast cancer proteomic data

Personalised body counter calibration using anthropometric parameters

This book describes the development of a new method for personalisation of efficiency factors in partial body counting. Its achieved goal is the quantification of uncertainties in those factors due to variation in anatomy of the measured persons, and their reduction by correlation with anthropometric parameters. The method was applied to a detector system at the In Vivo Measurement Laboratory at Karlsruhe Institute of Technology using Monte Carlo simulation and computational phantoms

Nonparametric Estimation of Distributional Functionals and Applications.

Distributional functionals are integrals of functionals of probability densities and include functionals such as information divergence, mutual information, and entropy. Distributional functionals have many applications in the fields of information theory, statistics, signal processing, and machine learning. Many existing nonparametric distributional functional estimators have either unknown convergence rates or are difficult to implement. In this thesis, we consider the problem of nonparametrically estimating functionals of distributions when only a finite population of independent and identically distributed samples are available from each of the unknown, smooth, d-dimensional distributions. We derive mean squared error (MSE) convergence rates for leave-one-out kernel density plug-in estimators and k-nearest neighbor estimators of these functionals. We then extend the theory of optimally weighted ensemble estimation to obtain estimators that achieve the parametric MSE convergence rate when the densities are sufficiently smooth. These estimators are simple to implement and do not require knowledge of the densities’ support set, in contrast with many competing estimators. The asymptotic distribution of these estimators is also derived. The utility of these estimators is demonstrated through their application to sunspot image data and neural data measured from epilepsy patients. Sunspot images are clustered by estimating the divergence between the underlying probability distributions of image pixel patches. The problem of overfitting is also addressed in both applications by performing dimensionality reduction via intrinsic dimension estimation and by benchmarking classification via Bayes error estimationPhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133394/1/krmoon_1.pd

Detection and identification of elliptical structure arrangements in images: theory and algorithms

Cette thèse porte sur différentes problématiques liées à la détection, l'ajustement et l'identification de structures elliptiques en images. Nous plaçons la détection de primitives géométriques dans le cadre statistique des méthodes a contrario afin d'obtenir un détecteur de segments de droites et d'arcs circulaires/elliptiques sans paramètres et capable de contrôler le nombre de fausses détections. Pour améliorer la précision des primitives détectées, une technique analytique simple d'ajustement de coniques est proposée ; elle combine la distance algébrique et l'orientation du gradient. L'identification d'une configuration de cercles coplanaires en images par une signature discriminante demande normalement la rectification Euclidienne du plan contenant les cercles. Nous proposons une technique efficace de calcul de la signature qui s'affranchit de l'étape de rectification ; elle est fondée exclusivement sur des propriétés invariantes du plan projectif, devenant elle même projectivement invariante. ABSTRACT : This thesis deals with different aspects concerning the detection, fitting, and identification of elliptical features in digital images. We put the geometric feature detection in the a contrario statistical framework in order to obtain a combined parameter-free line segment, circular/elliptical arc detector, which controls the number of false detections. To improve the accuracy of the detected features, especially in cases of occluded circles/ellipses, a simple closed-form technique for conic fitting is introduced, which merges efficiently the algebraic distance with the gradient orientation. Identifying a configuration of coplanar circles in images through a discriminant signature usually requires the Euclidean reconstruction of the plane containing the circles. We propose an efficient signature computation method that bypasses the Euclidean reconstruction; it relies exclusively on invariant properties of the projective plane, being thus itself invariant under perspective