Image Processing and Machine Learning Techniques for the Segmentation of cDNA

Image Processing and Machine Learning Techniques for the Segmentation of cDNA

Nikolaos Giannakeas (University of Ioannina, Greece) and Dimitrios I. Fotiadis (University of Ioannina, Greece)
DOI: 10.4018/978-1-60566-314-2.ch019
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Abstract

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.
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Introduction

Several types of microarrays have been developed to address different biological processes: (i) cDNA microarrays (Eisen, 1999) are used for the monitoring of the gene expression levels to study the effects of certain treatments, diseases, and developmental stages on gene expression. As a result, microarray gene expression profiling can be used to identify disease genes by comparing gene expression in diseased and normal cells. (ii) Comparative genomic hybridization application assesses genome content in different cells or closely related organisms (Pollack et al., 1999). (iii) SNP detection arrays identify single nucleotide polymorphism among alleles within or between populations (Moran & Whitney, 2004). (iv) Finally, Chromatin immunoprecipitation (chIP) technologies determine protein binding site occupancy throughout the genome, employing ChIP-on-chip technology (Buck & Lieb, 2004).

The experiment of cDNA microarrays typically starts by taking two biological tissues and extracting their mRNA. The mRNA samples are reverse transcribed into complementary DNA (cDNA) and labelled with fluorescent dyes resulting in a fluorescence-tagged cDNA. The most common dyes for tagging cDNA are the red fluorescent dye Cy5 (emission from 630-660 nm) and the green-fluorescent dye Cy3 (emission from 510-550 nm). Next, the tagged cDNA copy, called the sample probe, is hybridized on a slide containing a grid or array of single-stranded cDNAs called probes. Probes are usually known genes of interest which were printed on a glass microscope slide by a robotic arrayer. According to the hybridization principles, a sample probe will only hybridize with its complementary probe. The probe-sample hybridization process on a microarray typically occurs after several hours. All unhybridized sample probes are then washed off and the microarray is scanned twice, at different wavelengths corresponding to the different dyes used in the assay. The digital image scanner records the intensity level at each grid location producing two greyscale images. The intensity level is correlated with the absolute amount of RNA in the original sample, and thus, the expression level of the gene associated with this RNA.

Automated quantification of gene expression levels is realized analyzing the microarray images. Microarray images contain several blocks (or subgrids) which consist of a number of spots, placed in rows and columns (Figure 1). The level of intensity of each spot represents the amount of sample which is hybridized with the corresponding gene. The processing of microarray images (Schena et al., 1995) includes three stages: initially, spots and blocks are preliminarily located from the images (gridding). Second, using the available gridding information, each microarray spot is individually segmented into foreground and background. Finally, intensity extraction, calculates the foreground fluorescence intensity, which represents each gene expression level, and the background intensities. Ideally, the image analysis would be a rather trivial process, if all the spots had circular shape, similar size, and the background was noise and artefact free. However, a scanned microarray image has none of the above characteristics, thus microarray image analysis becomes a difficult task. In this chapter, we describe several microarray segmentation algorithms based on image processing and machine learning techniques.

Figure 1.

A Block of a Typical Microarray Image

Key Terms in this Chapter

Machine Learning: It refers to the design and development of algorithms and techniques that allow computers to “learn”. The purpose of machine learning is to extract information from several types of data automatically, using computational and statistical methods.

Block: Blocks are also known as grids or subgrids. These are areas of the microarray slide (and relatively of the microarray image) in which a number of spots are located.

Clustering: It is the task of decomposing or partitioning a dataset into groups so that the points in one group are similar to each other and are as different as possible from the points in the other groups.

Spot: It is a small and almost circular area in the microarray image whose mean intensity represents the expression level of the corresponding gene.

Classi fication: It is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items and based on a training set of previously labelled items.

Microarray: Sets of miniaturized chemical reaction areas that may also be used to test DNA fragments, antibodies, or proteins, by using a chip having immobilised target and hybridising them with a probed sample.

Image Processing: The analysis of an image using techniques that can identify shades, colours and relationships that cannot be perceived by the human eye. In the biomedical field, image processing is used to produce medical diagnosis or to extract data for further analysis.

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Table of Contents
Preface
Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis
Chapter 1
Ioannis Dimou, Michalis Zervakis, David Lowe, Manolis Tsiknakis
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Chapter 2
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Though an unparalleled amount and diversity of imaging and clinical data are now collected as part of routine care, this information is not... Sample PDF
Integrating Imaging and Clinical Data for Decision Support
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Chapter 3
Spyretta Golemati, John Stoitsis, Konstantina S. Nikita
The estimation of motion of the myocardial and arterial wall is important for the quantification of tissue elasticity and contractility and has... Sample PDF
Analysis and Quantification of Motion within the Cardiovascular System: Implications for the Mechanical Strain of Cardiovascular Structures
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Chapter 4
Christos V. Bourantas, Katerina Naka, Dimitrios Fotiadis, Lampros Michalis
Intracoronary Ultrasound (ICUS) imaging is an intravascular catheter-based technique which provides real-time, high resolution, cross-sectional... Sample PDF
New Developments in Intracoronary Ultrasound Processing
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Chapter 5
Stavroula Mougiakakou, Ioannis Valavanis, Alexandra Nikita, Konstantina S. Nikita
Recent advances in computer science provide the intelligent computation tools needed to design and develop Diagnostic Support Systems (DSSs) that... Sample PDF
Diagnostic Support Systems and Computational Intelligence: Differential Diagnosis of Hepatic Lesions from Computed Tomography Images
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Chapter 6
Marotesa Voultsidou, J. Michael Herrmann
Indicative features of an fMRI data set can be evaluated by methods provided by theory of random matrices (RMT). RMT considers ensembles of matrices... Sample PDF
Significance Estimation in fMRI from Random Matrices
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Chapter 7
Dimitrios C. Karampinos, Robert Dawe, Konstantinos Arfanakis, John G. Georgiadis
Diffusion Magnetic Resonance Imaging (diffusion MRI) can provide important information about tissue microstructure by probing the diffusion of water... Sample PDF
Optimal Diffusion Encoding Strategies for Fiber Mapping in Diffusion MRI
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Chapter 8
Dimitrios G. Tsalikakis, Petros S. Karvelis, Dimitrios I. Fotiadis
Segmentation plays a crucial role in cardiac magnetic resonance imaging (CMRI) applications, since it permits automated detection of regions of... Sample PDF
Segmentation of Cardiac Magnetic Resonance Images
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Chapter 9
Katia Marina Passera, Luca Tommaso Mainardi
Image registration is the process of determining the correspondence of features between images collected at different times or using different... Sample PDF
Image Registration Algorithms for Applications in Oncology
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Chapter 10
Lena Costaridou, Spyros Skiadopoulos, Anna Karahaliou, Nikolaos Arikidis, George Panayiotakis
Breast cancer is the most common cancer in women worldwide. Mammography is currently the most effective modality in detecting breast cancer... Sample PDF
Computer-Aided Diagnosis in Breast Imaging: Trends and Challenges
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Chapter 11
E. Kyriacou, C.I. Christodoulou, C. Loizou, M.S. Pattichis, C.S. Pattichis, S. Kakkos
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Chapter 12
Marios Neofytou, Constantinos Pattichis, Vasilios Tanos, Marios Pattichis, Eftyvoulos Kyriacou
The objective of this chapter is to propose a quantitative hysteroscopy imaging analysis system in gynaecological cancer and to provide the current... Sample PDF
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Chapter 13
Thomas V. Kilindris, Kiki Theodorou
Patient anatomy, biochemical response, as well functional evaluation at organ level, are key fields that produce a significant amount of multi modal... Sample PDF
Combining Geometry and Image in Biomedical Systems: The RT TPS Case
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Chapter 14
Ioannis Tsougos, George Loudos, Panagiotis Georgoulias, Konstantina S. Nikita, Kiki Theodorou
Quantitative three-dimensional nuclear medical imaging plays a continuously increasing role in radionuclide dosimetry, allowing the development of... Sample PDF
Internal Radionuclide Dosimetry using Quantitative 3-D Nuclear Medical Imaging
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Chapter 15
Evanthia E. Tripoliti, Dimitrios I. Fotiadis, Konstantia Veliou
Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain... Sample PDF
Diffusion Tensor Imaging and Fiber Tractography
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Chapter 16
Anastasios Koutlas, Dimitrios I. Fotiadis
The aim of this chapter is to analyze the recent advances in image processing and machine learning techniques with respect to facial expression... Sample PDF
Image Processing and Machine Learning Techniques for Facial Expression Recognition
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Chapter 17
Arcangelo Merla
This chapter presents an overview on recent developments in the field of clinical applications of the functional infrared imaging. The functional... Sample PDF
Developments and Advances in Biomedical Functional Infrared Imaging
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Chapter 18
Aristotelis Chatziioannou, Panagiotis Moulos
The completion of the Human Genome Project and the emergence of high-throughput technologies at the dawn of the new millennium, are rapidly changing... Sample PDF
DNA Microarrays: Analysis and Interpretation
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Chapter 19
Nikolaos Giannakeas, Dimitrios I. Fotiadis
Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical... Sample PDF
Image Processing and Machine Learning Techniques for the Segmentation of cDNA
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Chapter 20
Petros S. Karvelis, Dimitrios I. Fotiadis
Automated chromosome analysis is now becoming routine in most human cytogenetics laboratories. It involves both processing and analysis of digital... Sample PDF
Recent Advances in Automated Chromosome Image Analysis
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Chapter 21
O. Lezoray, G. Lebrun, C. Meurie, C. Charrier, A. Elmotataz, M. Lecluse
The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis.... Sample PDF
Machine Learning in Morphological Segmentation
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Chapter 22
Michael Haefner, Alfred Gangl, Michael Liedlgruber, A. Uhl, Andreas Vecsei, Friedrich Wrba
Wavelet-, Fourier-, and spatial domain-based texture classification methods have been used successfully for classifying zoom-endoscopic colon images... Sample PDF
Pit Pattern Classification Using Multichannel Features and Multiclassification
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Chapter 23
C. Papaodysseus, P. Rousopoulos, D. Arabadjis, M. Panagopoulos, P. Loumou
In this chapter the state of the art is presented in the domain of automatic identification and classification of bodies on the basis of their... Sample PDF
Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images
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Chapter 24
Alexia Giannoula, Richard S.C. Cobbold
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Chapter 25
Valentina Russo, Roberto Setola
The aim of this chapter is to provide an overview about models and methodologies used for the Dynamic Contrast Enhancement (DCE) analysis. DCE is a... Sample PDF
Dynamic Contrast Enhancement: Analysis's Models and Methodologies
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Chapter 26
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The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image... Sample PDF
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Chapter 27
Alberto Taboada-Crispi, Hichem Sahli, Denis Hernandez-Pacheco, Alexander Falcon-Ruiz
Various approaches have been taken to detect anomalies, with certain particularities in the medical image scenario, linked to other terms... Sample PDF
Anomaly Detection in Medical Image Analysis
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Chapter 28
C. Delgorge-Rosenberger, C. Rosenberger
The authors present in this chapter an overview on evaluation of medical image compression. The different methodologies used in the literature are... Sample PDF
Evaluation of Medical Image Compression
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Chapter 29
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Chapter 30
Farhang Sahba
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