Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images

Automatic Identification and Elastic Properties of Deformed Objects Using their Microscopic Images

C. Papaodysseus (National Technical University of Athens, Greece), P. Rousopoulos (National Technical University of Athens, Greece), D. Arabadjis (National Technical University of Athens, Greece), M. Panagopoulos (National Technical University of Athens, Greece) and P. Loumou (National Technical University of Athens, Greece)
DOI: 10.4018/978-1-60566-314-2.ch023
OnDemand PDF Download:


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 deformed images obtained via microscope. The approach is illustrated by means of the case of automatic recognition of third-stage larvae from microscopic images of them in high deformation instances. The introduced methodology incorporates elements of elasticity theory, image processing, curve fitting and clustering methods; a concise presentation of the state of the art in these fields is given. Combining proper elements of these disciplines, we first evaluate the undeformed shape of a parasite given a digital image of a random parasite deformation instance. It is demonstrated that different orientations and deformations of the same parasite give rise to practically the same undeformed shape when the methodology is applied to the corresponding images, thus confirming the consistency of the approach. Next, a pattern recognition method is introduced to classify the unwrapped parasites into four families, with a high success rate. In addition, the methodology presented here is a powerful tool for the exact evaluation of the mechano-elastic properties of bodies from images of their deformation instances.
Chapter Preview


Some Necessary Notions and Relations from Elasticity Theory

We now proceed to state some notions that are commonly used in most approaches of Elasticity theory (Chandrasekharaiah and Debnath, 1994). These will later be used in the analysis on which the unwrapping of the parasite is based.

Definition of the Stress and the Strain Tensor

Consider a one-to-one correspondence between all points of the deformed and the undeformed parasite states. Thus, let us consider two arbitrary points, say and of an undeformed parasite element and let and be their unique images in the deformed parasite body. In other words, due to the deformation, point moves to and point to . We consider the lengths of and : and

Now one defines the relative elongations along the x- and y-axes:

Along the x-axis, we set , in which case, and above become and , respectively. Thus, the relative elongation is defined to be


Similarly, along the y-axis we set and thus the relative elongation εyy is defined to be:


If the relative elongations are small, after expanding the square roots of (1) and (2) in Taylor series, we obtain

, (3).

Moreover, using the same assumption, the tangent of the angle of deformation of the x-axis, , and the y-axis,, become and respectively (Figure1). Adopting the hypothesis of small relative elongations, it results that , implying that the initially right angle is deformed by

(4) .
Figure 1.

Element differential deformation

Therefore, after using the shear stress definition , we adopt the standard strain tensor definition: .

Next, in order to study the elastic forces’ distribution throughout the parasite’s body, we proceed by considering an arbitrary differential element in the parasite body, starting at point with vertices, , , (Figure2). Let the force per unit area/length acting on the side be , where the first subscript denotes the axis to which the side is vertical, while the second subscript denotes the vector component axis. Similarly, . It is evident that the four functions , , , , suffice to determine the stress condition of the differential element under consideration. Hence, we define the standard stress tensor .

Figure 2.

Differential element strain forces

Hypothesis on the Parasite Constitutive Equation

The parasite constitutive equation relates the stress tensor with the strain tensor . These two tensors can be related through any functional form, i.e. . However, in many practical circumstances, this functional form can be considered to be linear, namely , where is a constant matrix (generalized Hooke ’s law).

Key Terms in this Chapter

Image Operations: Actions performed on an image that change the colour content of its pixels usually to detect or bring out some image characteristics.

Elastic Deformation Invariants: Quantities, shapes or characteristics of a body, e.g. a parasite, which remain invariant during its elastic deformation.

Automatic Curve Classification: A process which automatically classifies curves into different groups according to their similarity.

Parasite Image Segmentation: The automated procedure that isolates parasite body in its microscopic image and perhaps locates the various parasite body regions.

Curve Fitting Methods: Techniques that optimally fit a curve of desired functional form into a set of pixels or data points.

Parasite Mechano-Elastic Properties: The quantities and properties that characterize the body of a parasite, from the point of view of Mechanics and Elasticity Theory.

Pattern Classification Techniques: A set of methods that classify the members of a data set in different groups according to a number of group-characteristic patterns.

Complete Chapter List

Search this Book:
Editorial Advisory Board
Table of Contents
Themis P. Exarchos, Athanasios Papadopoulos, Dimitrios I. Fotiadis
Chapter 1
Ioannis Dimou, Michalis Zervakis, David Lowe, Manolis Tsiknakis
The automation of diagnostic tools and the increasing availability of extensive medical datasets in the last decade have triggered the development... Sample PDF
Computational Methods and Tools for Decision Support in Biomedicine: An Overview of Algorithmic Challenges
Chapter 2
William Hsu, Alex A.T. Bui, Ricky K. Taira, Hooshang Kangarloo
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
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
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
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
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
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
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
Chapter 11
E. Kyriacou, C.I. Christodoulou, C. Loizou, M.S. Pattichis, C.S. Pattichis, S. Kakkos
Stroke is the third leading cause of death in the Western world and a major cause of disability in adults. The objective of this work was to... Sample PDF
Assessment of Stroke by Analysing Cartoid Plaque Morphology
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
Quantitative Analysis of Hysteroscopy Imaging in Gynecological Cancer
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
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
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
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
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
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
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
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
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
“Elastography” or “elasticity imaging” can be defined as the science and methodology of estimating the mechanical properties of a medium (including... Sample PDF
Nonlinear Ultrasound Radiation-Force Elastography
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
Chapter 26
George K. Matsopoulos
The accurate estimation of point correspondences is often required in a wide variety of medical image processing applications including image... Sample PDF
Automatic Correspondence Methods towards Point-Based Medical Image Registration: An Evaluation Study
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
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
Chapter 29
Charalampos Doukas, Ilias Maglogiannis
Medical images are often characterized by high complexity and consist of high resolution image files, introducing thus several issues regarding... Sample PDF
Advanced ROI Coding Techniques for Medical Imaging
Chapter 30
Farhang Sahba
Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images... Sample PDF
Segmentation Methods in Ultrasound Images
About the Editors
About the Contributors