Recent Advances in Automated Chromosome Image Analysis

Recent Advances in Automated Chromosome Image Analysis

Petros S. Karvelis (University of Ioannina, Greece) and Dimitrios I. Fotiadis (University of Ioannina, Greece)
DOI: 10.4018/978-1-60566-314-2.ch020
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Automated chromosome analysis is now becoming routine in most human cytogenetics laboratories. It involves both processing and analysis of digital images and has been developed because of the demandby cytogeneticists. Over the years, many techniques have been introduced for the automatic segmentation and classification of chromosome images, of which only a few are included in the available commercial systems. Today, advances in chromosome imaging techniques, especially in multispectral imaging, lead the way for the development of new and improved methods for the location, segmentation and classification of chromosome images by exploiting the color information. In this chapter the authors describe methods which have been already developed for automated chromosome analysis.
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Chromosomes are structures that contain genes, which store in strings of DNA all the data necessary for an organism’s development and maintenance. They contain vast amounts of information; in fact each cell in a normal human being contains 46 chromosomes which have bits of information (Thompson, 1992). Chromosomes can only be examined visually during cell division (mitosis). They are extremely long and thin which make them essentially invisible. However, during the metaphase stage of mitosis, they contract and become much shorter (around 2–10μm) and wider (around 1–2 μm diameter), (Figure 1(a)). At this stage, they can be stained to become visible and can be imaged by a microscope.

Figure 1.

(a) A slide of grayscale banded chromosomes and (b) their karyotype.

Chromosome analysis is the procedure from which chromosomes are photographed during cell division and then are assigned to each class. This procedure is called karyotyping, where chromosomes are aligned in pairs in a tabular array as it is shown in Figure 1(b). Karyotyping is a useful tool to detect deviations from normal cell structure. Examples include peripheral blood, bone marrow, amniotic fluid, and products of conception. Normal human somatic cells have 46 chromosomes: 22 pairs of autosomes (chromosomes 1-22) and two sex chromosomes. Females carry two X chromosomes (46, XX), while males have a X and a Y (46, XY). Germ cells (egg and sperm) have 23 chromosomes: one copy of each autosome plus a single sex chromosome. This is referred to as the haploid number. One chromosome from each autosomal pair plus one sex chromosome is inherited from each parent. Mothers can contribute only an X chromosome to their children, while fathers can contribute either an X or a Y. Abnormal cells may have an excess or a deficit of chromosomes and/or structural defects which depict an exchange of genetic material.

Chromosome Abnormalities

Chromosome abnormalities can be very complex. There are two basic types of abnormalities: numerical and structural and both types can occur simultaneously. The most obvious abnormality is an unusual number of chromosomes. Having only one type of chromosome is a monosomy, such as Turner’s syndrome, in which there is only one X chromosome and no Y. Having three chromosomes is a trisomy, such as Down’s syndrome, in which there are three Type-21 chromosomes.

There can also be duplications of genetic material within a chromosome and translocations where two chromosomes exchange genetic information. The Philadelphia chromosome results from a translocation in the 9th and 22nd chromosomes. This is often associated with chronic myelogenous leukemia (Nowell, 1960). Detecting these abnormalities is vital because they are reliable indicators of genetic disease and damage. Chromosome abnormalities are particularly useful in cancer diagnosis and the related research (Gray, 1992).

Digital imaging has contributed to cytogenetics instrumentation reducing the workload in clinical labs and producing quantitative data for both research and diagnosis. The last few decades we have seen continuous endeavors in (a) the development of innovative image acquisition and enhancement methods on technologies that exploit our knowledge of the molecular basis of cancer or other diseases, and (b) the integration of these emerging genomic technologies with traditional imaging methods for more effective solutions for health care delivery. In this chapter we introduce the reader to the state of the art for automated methods in chromosome analysis.



The methods presented below are divided into two main categories based on the type of the image which is used.

Key Terms in this Chapter

Chromosome: A chromosome is a continuous piece of DNA, which contains many genes, regulatory elements and other nucleotide sequences.

Centromere Index: The centromere index is defined as the ratio of the length of the short arm of the chromosome divided by the length of the other arm.

Machine Learning: As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to “learn”.

Centromere: The centromere is a region, found in the middle of the chromosome, involved in cell division and the control of gene expression.

Class ification: The process of deriving a mathematical function that can predict the membership of a class based on input data.

Watershed: The segmentation based on watershed designs is a family of segmentation methods that consider an image as a topographic relief the flooding of which is simulated.

Complete Chapter List

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Editorial Advisory Board
Table of Contents
Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis
Chapter 1
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Computational Methods and Tools for Decision Support in Biomedicine: An Overview of Algorithmic Challenges
Chapter 2
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Integrating Imaging and Clinical Data for Decision Support
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
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
Chapter 5
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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
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
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
Chapter 8
Dimitrios G. Tsalikakis, Petros S. Karvelis, Dimitrios I. Fotiadis
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Segmentation of Cardiac Magnetic Resonance Images
Chapter 9
Katia Marina Passera, Luca Tommaso Mainardi
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Image Registration Algorithms for Applications in Oncology
Chapter 10
Lena Costaridou, Spyros Skiadopoulos, Anna Karahaliou, Nikolaos Arikidis, George Panayiotakis
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Computer-Aided Diagnosis in Breast Imaging: Trends and Challenges
Chapter 11
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Chapter 12
Marios Neofytou, Constantinos Pattichis, Vasilios Tanos, Marios Pattichis, Eftyvoulos Kyriacou
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Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer
Chapter 13
Thomas V. Kilindris, Kiki Theodorou
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Combining Geometry and Image in Biomedical Systems: The RT TPS Case
Chapter 14
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Internal Radionuclide Dosimetry using Quantitative 3-D Nuclear Medical Imaging
Chapter 15
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Diffusion Tensor Imaging and Fiber Tractography
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
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
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
Chapter 19
Nikolaos Giannakeas, Dimitrios I. Fotiadis
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Image Processing and Machine Learning Techniques for the Segmentation of cDNA
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
Chapter 21
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Chapter 22
Michael Haefner, Alfred Gangl, Michael Liedlgruber, A. Uhl, Andreas Vecsei, Friedrich Wrba
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Pit Pattern Classification Using Multichannel Features and Multiclassification
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
Chapter 24
Alexia Giannoula, Richard S.C. Cobbold
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Nonlinear Ultrasound Radiation-Force Elastography
Chapter 25
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Dynamic Contrast Enhancement: Analysis's Models and Methodologies
Chapter 26
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Automatic Correspondence Methods towards Point-Based Medical Image Registration: An Evaluation Study
Chapter 27
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Anomaly Detection in Medical Image Analysis
Chapter 28
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Evaluation of Medical Image Compression
Chapter 29
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Chapter 30
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