Cellular neural networks, Navier-Stokes equation and microarray image reconstruction

Copyright @ 2011 IEEE.Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier–Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time

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

A multi-view approach to cDNA micro-array analysis

The official published version can be obtained from the link below.Microarray has emerged as a powerful technology that enables biologists to study thousands of genes simultaneously, therefore, to obtain a better understanding of the gene interaction and regulation mechanisms. This paper is concerned with improving the processes involved in the analysis of microarray image data. The main focus is to clarify an image's feature space in an unsupervised manner. In this paper, the Image Transformation Engine (ITE), combined with different filters, is investigated. The proposed methods are applied to a set of real-world cDNA images. The MatCNN toolbox is used during the segmentation process. Quantitative comparisons between different filters are carried out. It is shown that the CLD filter is the best one to be applied with the ITE.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the National Science Foundation of China under Innovative Grant 70621001, Chinese Academy of Sciences under Innovative Group Overseas Partnership Grant, the BHP Billiton Cooperation of Australia Grant, the International Science and Technology Cooperation Project of China under Grant 2009DFA32050 and the Alexander von Humboldt Foundation of Germany

Image-based quantitative analysis of gold immunochromatographic strip via cellular neural network approach

"(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time

Προδιαθεσικοί παράγοντες παχυσαρκίας σε παιδιά με ΔΕΠΥ και παιδιά χωρίς ΔΕΠΥ

Εισαγωγή : Η Διαταραχή ελλειμματικής προσοχής – Υπερκινητικότητα είναι η συχνότερη νευροβιολογική διαταραχή της παιδικής ηλικίας και χαρακτηρίζεται από ελλειμματική προσοχή, υπερκινητικότητα και παρορμητικότητα. Σε πολλές μελέτες έχει βρεθεί συσχέτιση μεταξύ ΔΕΠΥ και παχυσαρκίας. Σκοπός: Σκοπός της παρούσας εργασίας ήταν η διερεύνηση των παραγόντων που προδιαθέτουν σε παχυσαρκία σε παιδιά με ΔΕΠΥ και σε παιδιά τυπικής ανάπτυξης. Υλικό και μέθοδος: Το δείγμα αποτέλεσαν 70 παιδιά ηλικίας 6 έως 18 ετών εκ των οποίων 40 είχαν διαγνωστεί με ΔΕΠΥ βάσει DSM-5 κριτηρίων και τα υπόλοιπα 30 επελέγησαν με απλή τυχαία δειγματοληψία από σχολεία πρωτοβάθμιας και δευτεροβάθμιας εκπαίδευσης. Τα δεδομένα που συλλέχθηκαν αφορούν στις διατροφικές συνήθειες, σε περιγεννητικούς παράγοντες, τρόπο ζωής και κοινωνικοοικονομικό υπόβαθρο. Εφαρμόστηκε πολυπαραγοντική λογαριθμιστική παλινδρόμηση και χρησιμοποιήθηκε για την ανάλυση των δεδομένων το στατιστικό πρόγραμμα SPSS και ετέθη ως επίπεδο στατιστικής σημαντικότητας το 0,05. Αποτελέσματα: Το ποσοστό παιδιών με ΔΕΠΥ που ήταν υπέρβαρα – παχύσαρκα ήταν 32,5% και φυσιολογικού βάρους 67,5%. Αντίστοιχα, στα παιδιά τυπικής ανάπτυξης τα ποσοστά ήταν 13,3% και 86,7%. Ως παράγοντες κινδύνου για παχυσαρκία σε παιδιά με ΔΕΠΥ αναγνωρίστηκε το χαμηλό KIDMED score(0.048) και η διάρκεια κύησης κάτω των 37 εβδομάδων(p=0,035). Ενώ στα αγόρια με ΔΕΠΥ επιπλέον παράγοντα αποτελεί το υψηλό μορφωτικό επίπεδο της μητέρας. Όσον αφορά τα παιδιά τυπικής ανάπτυξης ο μόνος παράγοντας που φαίνεται να σχετίζεται με την παχυσαρκία είναι το KIDMED score(p=0.04). Συμπεράσματα: Η υιοθέτηση μεσογειακής διατροφής συμβάλλει στην πρόληψη της παιδικής παχυσαρκίας. Επιπλέον στα παιδιά με ΔΕΠΥ, η διάρκεια κύησης και το μορφωτικό επίπεδο της μητέρας φαίνεται να παίζουν ρόλο.Περαιτέρω διερεύνηση των πιθανών παραγόντων κινδύνου της παιδικής παχυσαρκίας, κυρίως σε παιδιά με ΔΕΠΥ θα συμβάλλει στη πρόληψη της και στη βελτίωση της υγείας τους.Introduction: ADHD is the most common neurobiological disorder of childhood. Its main features are attention deficit, hyperactivity and impulsivity. In many studies there seems to be an association between ADHD and obesity. Obesity has emerged as a major health issue for scientists all over the world. Aim: The aim of this study was to investigate obesity-inducing factors in children with ADHD and in typically developed children. Material & Methods: The sample consisted of 70 children aged 6 to 18 years old , of which, 40 were diagnosed with ADHD, based on DSM-5 criteria, and the rest, 30 children constituting the control group were selected by simple , random sampling of primary and secondary schools. The data collected relates to dietary habits, perinatal factors, lifestyle, and socio-economic background. Multivariate logistic regression was applied, and the SPSS statistical program was used to analyze the data. The statistical significance level was set at 0.05. Results: The percentage of children with ADHD who were overweight – obese, was 32.5% and in typically developed children, was 13.3%. As a risk factor for obesity in children with ADHD, the KIDMED score (0.048) and the gestational age (p = 0.035) were identified. In children with gestational age over 37 weeks, the chance of being overweight-obese is reduced by 82%. At the same time, as the KIDMED score increases, the chance of the child being overweight-obese decreases. Boys with ADHD whose mothers had a university degree - postgraduate studies were more likely to be overweight – obese. As about standard development children the only factor that appears to be related to obesity is the KIDMED score. Conclusion: Adopting a Mediterranean diet contributes substantially to the prevention of childhood obesity in children with ADHD and children with typical development. In addition, in children with ADHD, gestational age and their mothers educational level appear to be important factors. Further investigation of the possible risk factors for childhood obesity, especially in children with ADHD, will help prevent obesity in these children and improve their health

A novel neural network approach to cDNA microarray image segmentation

This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

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