Microarray image processing: A novel neural network framework

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.Aleppo University, Syri

Design and Real-World Application of Novel Machine Learning Techniques for Improving Face Recognition Algorithms

Recent progress in machine learning has made possible the development of real-world face recognition applications that can match face images as good as or better than humans. However, several challenges remain unsolved. In this PhD thesis, some of these challenges are studied and novel machine learning techniques to improve the performance of real-world face recognition applications are proposed. Current face recognition algorithms based on deep learning techniques are able to achieve outstanding accuracy when dealing with face images taken in unconstrained environments. However, training these algorithms is often costly due to the very large datasets and the high computational resources needed. On the other hand, traditional methods for face recognition are better suited when these requirements cannot be satisfied. This PhD thesis presents new techniques for both traditional and deep learning methods. In particular, a novel traditional face recognition method that combines texture and shape features together with subspace representation techniques is first presented. The proposed method is lightweight and can be trained quickly with small datasets. This method is used for matching face images scanned from identity documents against face images stored in the biometric chip of such documents. Next, two new techniques to increase the performance of face recognition methods based on convolutional neural networks are presented. Specifically, a novel training strategy that increases face recognition accuracy when dealing with face images presenting occlusions, and a new loss function that improves the performance of the triplet loss function are proposed. Finally, the problem of collecting large face datasets is considered, and a novel method based on generative adversarial networks to synthesize both face images of existing subjects in a dataset and face images of new subjects is proposed. The accuracy of existing face recognition algorithms can be increased by training with datasets augmented with the synthetic face images generated by the proposed method. In addition to the main contributions, this thesis provides a comprehensive literature review of face recognition methods and their evolution over the years. A significant amount of the work presented in this PhD thesis is the outcome of a 3-year-long research project partially funded by Innovate UK as part of a Knowledge Transfer Partnership between University of Hertfordshire and IDscan Biometrics Ltd (partnership number: 009547)

Microarray image processing : a novel neural network framework

Due to the vast success of bioengineering techniques, a series of large-scale analysis tools has been developed to discover the functional organization of cells. Among them, cDNA microarray has emerged as a powerful technology that enables biologists to cDNA microarray technology has enabled biologists to study thousands of genes simultaneously within an entire organism, and thus obtain a better understanding of the gene interaction and regulation mechanisms involved. Although microarray technology has been developed so as to offer high tolerances, there exists high signal irregularity through the surface of the microarray image. The imperfection in the microarray image generation process causes noises of many types, which contaminate the resulting image. These errors and noises will propagate down through, and can significantly affect, all subsequent processing and analysis. Therefore, to realize the potential of such technology it is crucial to obtain high quality image data that would indeed reflect the underlying biology in the samples. One of the key steps in extracting information from a microarray image is segmentation: identifying which pixels within an image represent which gene. This area of spotted microarray image analysis has received relatively little attention relative to the advances in proceeding analysis stages. But, the lack of advanced image analysis, including the segmentation, results in sub-optimal data being used in all downstream analysis methods. Although there is recently much research on microarray image analysis with many methods have been proposed, some methods produce better results than others. In general, the most effective approaches require considerable run time (processing) power to process an entire image. Furthermore, there has been little progress on developing sufficiently fast yet efficient and effective algorithms the segmentation of the microarray image by using a highly sophisticated framework such as Cellular Neural Networks (CNNs). It is, therefore, the aim of this thesis to investigate and develop novel methods processing microarray images. The goal is to produce results that outperform the currently available approaches in terms of PSNR, k-means and ICC measurements.EThOS - Electronic Theses Online ServiceAleppo University, SyriaGBUnited Kingdo

Massively Parallel Approach to Modeling 3D Objects in Machine Vision

Electrical Engineerin

Generalização cartográfica com recurso a inteligência artificial

Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Sistemas de Informação Geográfica), Universidade de Lisboa, Faculdade de Ciências, 2015As agências cartográficas nacionais estão institucionalmente comprometidas com a produção multi-escala que exige a manutenção de enormes volumes de dados de diferentes resoluções espaciais e temporais de difícil gestão e manutenção. A alternativa a este ciclo crescente do volume de dados reside na implementação de sistemas de generalização automática que permitam reduzir a produção e manutenção de uma única série cartográfica de maior exatidão geométrica e semântica. Neste trabalho apresenta-se o processo de desenvolvimento e implementação de uma infraestrutura de generalização cartográfica automática de linhas baseada numa estratégia de constrangimento e refinamento iterativo com recurso a algoritmos de inteligência artificial. Considera-se um algoritmo paramétrico de simplificação e suavização de linhas aplicado ao caso particular de curvas de nível. A automatização do processo de generalização cartográfica de linhas passa por selecionar de forma automática o parâmetro a utilizar no algoritmo, a tensão a aplicar à linha. A proposta consiste no recurso a técnicas de inteligência artificial para seleção do valor ótimo do parâmetro tensão. É utilizada uma Rede Neuronal, uma Árvore de Decisão e uma Árvore de Classificação e Regressão, para o cálculo do valor da tensão, obtidos os valores preditos por estes métodos é escolhido o ótimo entre eles através de um Agente que executa um esquema de leilão. A metodologia aplicada inicia-se com a caracterização numérica das curvas de nível, calculando entre outras, a dimensão fractal, comprimento, área, angularidade, a partir das quais é calculado o valor da tensão a usar no algoritmo, para cada curva de nível. Por fim são apresentados dois algoritmos para contextualização da generalização cartográfica de curvas de nível com os pontos de cota, vértices geodésicos e linhas de água, respeitando as leis de Brisson.The National Mapping Agencies are institutionally compromised with the multi-scale production that requires the maintenance of enormous amount of data with different space and time resolutions increasing the management and maintenance. The alternative to this cycle of data increasing must be the implementation of automatic cartographic generalization systems that will lead to a significant reduction in the data amount and data maintenance in a unique cartographic series of higher geometric and semantic accuracy. In this work, a new cartographic generalization infrastructure is presented. The infrastructure was developed for the cartographic generalization of lines and is based on the constraint and iterative strategy using artificial intelligence algorithms. We have considered a parametric algorithm for line simplification and smooth applied to the particular case of contour lines. The automation of the process is based on the intelligent selection of the parameter to be used for the line generalization. The parameter is the tension to be applied to the line that is elected among all possible values using artificial intelligence techniques. A Neuronal Net, a Decision Tree and a Classification and Regression Tree, are used for the selection of the tension to be applied to the curve that are further auctioned through an Agent that selects the best tension value. The applied methodology is initiated with the numerical characterization of the contour lines, calculating among others, the fractal dimension, length, area, angularity, from which the value of the tension is calculated to use in the algorithm, for each contour line. Finally, two algorithms for the contextualization of the generalized contour lines with the altimetry points are presented, geodesic vertices and water lines

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine

Intelligent X-ray imaging inspection system for the food industry.

The inspection process of a product is an important stage of a modern production factory. This research presents a generic X-ray imaging inspection system with application for the detection of foreign bodies in a meat product for the food industry. The most important modules in the system are the image processing module and the high-level detection system. This research discusses the use of neural networks for image processing and fuzzy-logic for the detection of potential foreign bodies found in x-ray images of chicken breast meat after the de-boning process. The meat product is passed under a solid-state x-ray sensor that acquires a dual-band two-dimensional image of the meat (a low- and a high energy image). A series of image processing operations are applied to the acquired image (pre-processing, noise removal, contrast enhancement). The most important step of the image processing is the segmentation of the image into meaningful objects. The segmentation task is a difficult one due to the lack of clarity of the acquired X-ray images and the resulting segmented image represents not only correctly identified foreign bodies but also areas caused by overlapping muscle regions in the meat which appear very similar to foreign bodies in the resulting x-ray image. A Hopfield neural network architecture was proposed for the segmentation of a X-ray dual-band image. A number of image processing measurements were made on each object (geometrical and grey-level based statistical features) and these features were used as the input into a fuzzy logic based high-level detection system whose function was to differentiate between bones and non-bone segmented regions. The results show that system's performance is considerably improved over non-fuzzy or crisp methods. Possible noise affecting the system is also investigated. The proposed system proved to be robust and flexible while achieving a high level of performance. Furthermore, it is possible to use the same approach when analysing images from other applications areas from the automotive industry to medicine

Features and statistical classifiers for face image analysis

This thesis presents the systematic analysis of feature spaces and classification schemes for face image processing. Linear discriminants, probabilistic classifiers, and nearest neighbour classifiers are applied to face/nonface classification in various feature spaces including original grayscale space, face-image-whitened space, anything-image-whitened space, and double-whitened space. According to the classification error rates, the probabilistic classifiers performed the best, followed by nearest neighbour classifiers, and then the linear discriminant classifier. However, the former two kinds of classifiers are more computationally demanding. No matter what kind of classifier is used, the whitened space with reduced dimensionality improves classification performance. -- A new feature extraction technique, named dominant feature extraction, is invented and applied to face/nonface classification with encouraging results. This technique extracts the features corresponding to the mean-difference and variance-difference of two classes. Other classification schemes, including the repeated Fisher's Linear Discriminant (FLD) and a moving-centre scheme, are newly proposed and tested. The Maximum Likelihood (ML) classifier based on hyperellipsoid distribution is applied for the first time to face/nonface classification. -- Face images are conventionally represented by grayscales. This work presents a new representation that includes motion vectors, obtained through optical flow analysis between an input image and a neutral template, and a deformation residue that is the difference between the deformed input image and the template. The face images compose a convex cluster in this representation space. The viability of this space is tested and demonstrated through classification experiments on face detection, expression analysis, pose estimation, and face recognition. When the FLD is applied to face/nonface classification and smiling/nonsmiling face classification, the new representation of face images outperforms the traditional grayscale representation. Face recognition experiments using the nearest neighbour classifier on the Olivetti and Oracle Research Laboratory (ORL) face database shows that the deformation residue representation is superior to all other representations. These promising results demonstrate that as a general-purpose space, the derived representation space is suitable for almost all aspects of face image processing