Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Pre-Procesamiento de Imágenes de Electroforesis Bidimensional en Gel Aplicado al Análisis Proteómico

El uso de la proteomica es uno de los topicos de investigacion mas activos en la biotecnologia, la cual se refiere a toda aplicación tecnológica que utilice sistemas biológicos y organismos vivos o sus derivados para la creacion o modi ficacion de productos o procesos para usos específicos. Dentro de la proteomica, la electroforesis bidimensional en gel (2DGE) es la herramienta mas utilizada para la separacion e identi cacion de proteínas. Esta técnica se ha mantenido como la forma m as viable de obtener resultados con ables y reproducibles en el analisis proteomico. En la actualidad, existen numerosos metodos y software para el procesamiento de imágenes de geles obtenidos a partir de la electroforesis bidimensional. Sin embargo, no existe aun un sistema able, automatizado y de alta reproducibilidad, debido a las anomalías presentes en este tipo de imagenes. Entre las anomalías mas comunes se encuentran líneas verticales, puntos difusos y el ruido, las cuales di ficultan el procesamiento de las imagenes. Este proyecto de investigacion desarrollo una metodología de pre-procesamiento de imagenes de geles bidimensionales aplicada al analisis proteomico, utilizando tecnicas deprocesamiento digital de imagenes como normalizacion de imagen, correccion de fondo y ltrado no lineal. Para el desarrollo de este trabajo se realiz o una comparaci on cuantitativa y cualitativa de diferentes tecnicas de pre-procesamiento utilizando imagenes 2DGE tanto sinteticas como reales. A partir de la comparacion de las tecnicas de pre-procesamiento encontradas en la literatura, se propuso una metodología multinivel, que permite mitigar diferentes tipos de anomalías de las imágenes 2DGE. Con la metodología multinivel propuesta se logro una mejora del 34% respecto a la aplicacion de tecnicas de manera individual sobre imagenes 2DGE sinteticasProteomics is one of the most active research areas in biotechnology due to its wide, diverse and important applications. High resolution two-dimensional gel electrophoresis (2DGE) is a key technique for protein separation and analysis. With this technique it is possible to btain reliable and reproducible results for proteomic analysis. Currently, there are several methods and software for processing two-dimensional gel electrophoresis images. However, there is still not a reliable, automated and highly reproducible system for this, due to the anomalies present in this type of image. The most common anomalies present in 2DGE images are vertical streaking, noise, and low abundance spots, which complicate the image processing. In this research project a methodology for pre-processing two-dimensional gel images applied to proteomic analysis has been applied, using digital image processing techniques such as normalization, background correction and non-linear ltering. A quantitative and qualitative comparison was carried out of several pre-processing techniques using real and synthetic 2DGE images. Based on the results of this comparison of pre-processing techniques for background correction, normalization and noise reduction, a new multilevel methodology is proposed, that reduces the e ects of several types of anomalies on 2DGE images. The proposed combined methodology displays a 34% improvement compared to individual pre-processing techniques on 2DGE synthetic imagesMagister en Automatización y Contro

Técnicas basadas en kernel para el análisis de texturas en imagen biomédica

[Resumen] En problemas del mundo real es relevante el estudio de la importancia de todas las variables obtenidas de manera que sea posible la eliminación de ruido, es en este punto donde surgen las técnicas de selección de variables. El objetivo de estas técnicas es pues encontrar el subconjunto de variables que describan de la mejor manera posible la información útil contenida en los datos permitiendo mejorar el rendimiento. En espacios de alta dimensionalidad son especialmente interesantes las técnicas basadas en kernel, donde han demostrado una alta eficiencia debido a su capacidad para generalizar en dichos espacios. En este trabajo se realiza una nueva propuesta para el análisis de texturas en imagen biomédica mediante la integración, utilizando técnicas basadas en kernel, de diferentes tipos de datos de textura para la selección de las variables más representativas con el objetivo de mejorar los resultados obtenidos en clasificación y en interpretabilidad de las variables obtenidas. Para validar esta propuesta se ha formalizado un diseño experimental con cuatro fases diferenciadas: extracción y preprocesado de los datos, aprendizaje y selección del mejor modelo asegurando la reproducibilidad de los resultados a la vez que una comparación en condiciones de igualdad.[Resumo] En problemas do mundo real é relevante o estudo da importancia de todas as variables obtidas de maneira que sexa posible a eliminación de ruído, é neste punto onde xorden as técnicas de selección de variables. O obxectivo destas técnicas é pois encontrar o subconxunto de variables que describan do mellor xeito posible a información útil contida nos datos permitindo mellorar o rendemento. En espazos de alta dimensionalidade son especialmente interesantes as técnicas baseadas en kernel, onde demostraron unha alta eficiencia debido á súa capacidade para xeneralizar nos devanditos espazos. Neste traballo realízase unha nova proposta para a análise de texturas en imaxe biomédica mediante a integración, utilizando técnicas baseadas en kernel, de diferentes tipos de datos de textura para a selección das variables máis representativas co obxectivo de mellorar os resultados obtidos en clasificación e en interpretabilidade das variables obtidas. Para validar esta proposta formalizouse un deseño experimental con catro fases diferenciadas: extracción e preprocesar dos datos, aprendizaxe e selección do mellor modelo asegurando a reproducibilidade dos resultados á vez que unha comparación en condicións de igualdade. Utilizáronse imaxes de xeles de electroforese bidimensional.[Abstract] In real-world problems it is of relevance to study the importance of all the variables obtained, so that denoising could be possible, because it is at this point when the variable selection techniques arise. Therefore, these techniques are aimed at finding the subset of variables that describe' in the best possible way the useful information contained in the data, allowing improved performance. In high-dimensional spaces, the kernel-based techniques are of special relevance, as they have demonstrated a high efficiency due to their ability to generalize in these spaces. In this work, a new approach for texture analysis in biomedical imaging is performed by means of integration. For this procedure, kernel-based techniques were used with different types of texture data for the selection of the most representative variables in order to improve the results obtained in classification and interpretability of the obtained variables. To validate this proposal, an experimental design has been concluded, consisting of four different phases: 1) Data extraction; 2) Data pre-processing; 3) Learning and 4) Selection of the best model to ensure the reproducibility of results while making a comparison under conditions of equality. In this regard, two-dimensional electrophoresis gel images have been used

Bacterial image analysis based on time-lapse microscopy

H time-lapse μικροσκοπία επιτρέπει πλέον τη λεπτομερή δημιουργία δεδομένων από δυναμικές κυτταρικές διεργασίες σε επίπεδο μεμονωμένων κυττάρων (single cell level). Πρόσφατες μελέτες έχουν τονίσει τη χρήση και τη σημασία αυτής της τεχνολογίας για τη διερεύνηση του βιολογικού θορύβου στην έκφραση γονιδίων και των βιολογικών μονοπατιών. Μαθηματικά και στατιστικά μοντέλα για την περιγραφή και τον έλεγχο υποθέσεων σχετικά με τη δυναμική των βακτηριακών κοινοτήτων βασίζονται στην ανάλυση των "ταινιών" time-lapse εικόνων. Ωστόσο, η ανάλυση των ακολουθιών εικόνας είναι πολύ χρονοβόρα και επιρρεπής σε λάθη, δεδομένου ότι απαιτεί ακόμα και σήμερα σε μεγάλο βαθμό τη συμμετοχή ενός ανθρώπου εμπειρογνώμονα. Έχουμε αναπτύξει μία σωλήνωση (pipeline) αλγορίθμων που προσδιορίζει τα όρια των επιμέρους κυττάρων (βακτηριακή κατάτμηση) και τα ανιχνεύει στην πάροδο του χρόνου (ανίχνευση βακτηρίων), ακόμη και σε μεγάλου μεγέθους αποικίες μικροβίων, όπου υπάρχει μεγάλη δυσκολία στον εντοπισμό των ορίων μεμονωμένων κυττάρων. Η μεθοδολογία μας συνδυάζει προηγμένες τεχνικές από την επεξεργασία εικόνας και τη μηχανική μάθηση για την κατάτμηση των βακτηρίων και την παρακολούθηση της ανάπτυξης μιας αποικίας (κατασκευή δέντρου γενεαλογίας). Επιπλέον, είναι πλήρως αυτοματοποιημένη, υπολογιστικά αποδοτική και κατάλληλη για υψηλής ρυθμαπόδοσης ανάλυση χωρίς να απαιτείται παρέμβαση του χρήστη.Time-lapse microscopy now enables the continuous monitoring of dynamic cellular processes at the single cell level. Recent studies have highlighted the use and importance of this technology for investigating biological noise in genes expression and the behavior of biological pathways. Mathematical and statistical models for describing and testing hypotheses regarding the dynamics of bacterial communities rely on the analysis of time-lapse image "movies". However, the analysis of image sequences is very time consuming and error prone since it currently requires the heavy involvement of a human expert. We developed a pipeline of algorithms for determining the boundaries of individual cells (bacterial segmentation) and follow them over time (bacterial tracking), even in large-size microbial colonies, where there is great difficulty in identifying individual cell boundaries. Our methodology combines advanced image processing and machine learning techniques for segmentation of bacteria and monitoring the development of the colony (construction of the lineage tree). Furthermore it is fully automated, computationally efficient and suitable for high throughput image analysis without requiring any user intervention

image analysis and processing with applications in proteomics and medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours. The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization. The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics

Image Analysis and Processing With Applications in Proteomics and Medicine

Στην παρούσα διατριβή παρουσιάζονται αυτόματοι αλγόριθμοι ανάλυσης εικόνας για την κατάτμηση διαφόρων τύπων εικόνων, με έμφαση στις εικόνες πρωτεομικής και στις ιατρικές εικόνες. Οι προτεινόμενοι αλγόριθμοι βασίζονται στις αρχές των παραμορφώσιμων μοντέλων. Η διατριβή εστιάζει σε δύο κυρίως στόχους: 1) στην επίλυση του σημαντικού προβλήματος της αυτόματης παραμετροποίησης στην κατάτμηση εικόνας, 2) στην διατύπωση ενός ολοκληρωμένου μοντέλου κατάτμησης εικόνων πρωτεομικής. Η πρώτη συνεισφορά είναι ένα πρωτότυπο πλαίσιο αυτόματης παραμετροποίησης των ενεργών περιγραμμάτων περιοχής. Το πλαίσιο εμπλουτίζει τα αποτελέσματα με αντικειμενικότητα και απελευθερώνει τους τελικούς χρήστες από την επίπονη διαδικασία της εμπειρικής ρύθμισης. Εφαρμόζεται σε διάφορους τύπους ιατρικών εικόνων και παραμένει ανεπηρέαστο στις τροποποιήσεις των ρυθμίσεων των συσκευών λήψης των εικόνων αυτών. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο πλαίσιο διατηρεί υψηλή την ποιότητα κατάτμησης, συγκρίσιμη με εκείνη που επιτυγχάνεται με εμπειρική παραμετροποίηση. Η δεύτερη συνεισφορά είναι ένα αυτόματο μοντέλο βασιζόμενο στα ενεργά περιγράμματα για την κατάτμηση εικόνων πρωτεομικής. Το μοντέλο αντιμετωπίζει σημαντικά προβλήματα συμπεριλαμβανομένων των γραμμών, τεχνουργημάτων, αχνών και επικαλυπτομένων κηλίδων. Ακόμη, παρέχει εναλλακτική λύση στην επιρρεπή σε σφάλματα διαδικασία της χειρωνακτικής επεξεργασίας που απαιτείται στα υπάρχοντα πακέτα λογισμικού. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο μοντέλο υπερτερεί των υπαρχόντων πακέτων λογισμικού σε ποσοτικές μετρικές εντοπισμού και κατάτμησης.This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models. Two objectives are pursued: 1) the core issue of unsupervised parameterization in image segmentation, 2) the formulation of a complete model for the segmentation of proteomics images. The first contribution is a novel framework for automated parameterization of region-based active contours. The presented framework endows segmentation results with objectivity and sets domain users free from the cumbersome process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of acquisition devices. The experimental results demonstrate that the presented framework maintains a high segmentation quality, comparable to the one obtained with empirical parameterization. The second contribution is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues including streaks, artifacts, faint and overlapping spots. Moreover, it provides an alternate to the error-prone process of manual editing, required in state-of-the-art software packages. The experimental results demonstrate that the proposed model outperforms software packages in terms of detection and segmentation quantity metrics