Computer Vision for X-Ray Testing: Imaging, Systems, Image Databases, and Algorithms

362 Pages·2015·17.79 MB·English

by  Domingo Mery (auth.)

Domingo Mery
Computer
Vision for
X-Ray Testing
Imaging, Systems, Image Databases,
and Algorithms
Computer Vision for X-Ray Testing
Domingo Mery
Computer Vision for X-Ray
Testing
Imaging, Systems, Image Databases,
and Algorithms
123
DomingoMery
Pontificia Universidad Católica deChile
Santiago
Chile
ISBN978-3-319-20746-9 ISBN978-3-319-20747-6 (eBook)
DOI 10.1007/978-3-319-20747-6
LibraryofCongressControlNumber:2015942783
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To Ximena, Anais and Valeria
who show me everyday
the X-rays of love
Foreword
The wavelengths of X-rays are far shorter than those of visible light, and even
shorter than those of ultraviolet light. Wilhelm Conrad Röntgen (1845–1923) was
awarded the first Nobel prize in Physics in 1901 for his contributions to the
detectionofelectromagneticradiation,andtothegenerationofX-rays,whicharea
form of electromagnetic radiation. Radiographs are produced by having X-rays,
emitted from a source, geometrically assumed to be a point in three-dimensional
(3D)space,recordedonascreen.Thisscreenmighthaveaslightlycurvedsurface,
but we can also see it (via defined mapping) as an image plane.
X-ray technology provides a way to visualize the inside of visually opaque
objects.Pixelintensitiesinrecordedradiographscorrespondbasicallytothedensity
of matter, integrated along rays; those readers interested in a more accurate
description may wish to look up the interaction of X-rays with matter by way of
photo-absorption, Compton scattering, or Rayleigh scattering by reading the first
chapter of this book.
X-ray technology aims at minimizing scattering, by having nearly perfect rays
passthroughthestudiedobject.Thus,wehaveaveryparticularimagingmodality:
objects of study need to fit into a bounded space, defined as being between source
and image plane, and pixel intensities have a meaning which differs from our
commonly recorded digital images when using optical cameras.
When modeling an X-ray imaging system we can apply much of the projective
geometry, mathematics in homogeneous spaces, or analogous parameter notations:
wejustneedtobeawarethatwearelooking“backwards,”fromtheimageplaneto
the source (known as projection center), and no longer from the image plane into
thepotentiallyinfinitespaceinfrontofanopticalcamera.Thus,itappearsthatthe
problem of understanding 3D objects is greatly simplified by simply studying a
bounded space: using a finite number of source-plus-screen devices for recording
this bounded space; applying photogrammetric methods for understanding
multi-view recordings, and applying the proper interpretation (e.g., basically den-
sity) to the corresponding pixel values. Thus, this very much follows a common
scenario of a computer vision, while also including image preprocessing and
vii
viii Foreword
segmentation, object detection, and classification. The book addresses all of these
subjects in the particular context of X-ray testing based on computer vision.
The briefly sketched similarities between common (i.e., optical-camera-based)
computer vision and X-ray testing techniques might be a good motif to generate
curiosity among people working in computer vision, in order to understand how
their knowledge can contribute to, or benefit from, various methods of X-ray
testing.
ThebookillustratesX-raytestingforaninterestingrangeofapplications.Italso
introducesapublicallyavailablesoftwaresystemandanextensiveX-raydatabase.
The book will undoubtedly contribute to the popularity of X-ray testing among
thoseinthecomputervisionandimageanalysiscommunity,andmayalsoserveas
a textbook or as support material for undertaking related research.
Auckland Reinhard Klette
April 2015
Preface
Thisbookhasbeenwritteninmanyspatiotemporalcoordinates.Forinstance,some
equationsandfigureswereperformedduringmyPh.D.attheTechnicalUniversity
of Berlin (1996–2000). During that period, but in Hamburg, I took several X-ray
images—that have been used in this book—in YXLON X-ray International Labs.
After completing my Ph.D., and during my work in Santiago, Chile as associate
researcherattheUniversity ofSantiagoofChile (2001–2003) andfacultymember
at the Catholic University of Chile (2004–to date) I have written more than 40
journalpapersoncomputervisionappliedtoX-raytesting.Duringthistime,Ihave
developedaMatlab Toolboxthathasbeenusedinmyresearchprojectsandinmy
classes teaching image processing, pattern recognition and computer vision for
graduateandundergraduatestudents.Overthelastfewyears,mygraduatestudents
have taken thousands of X-ray images in our X-ray Testing Lab at the Catholic
University of Chile. Moreover, in my sabbatical year at the University of Notre
Dame (2014–2015), I had the time and space to teach the computer vision course
for students of computer sciences, electrical engineering, and physics, and I have
been able to bring together all those related papers, diagrams, and codes in this
book.
The present work has been written not only in three different countries
(Germany, Chile, and the United States) over the last 15 years, but also in many
different small places that provided me with the time and peace to write a para-
graph,acaptionofafigure,acode,orwhateverIcould.Forexample,Iremembera
Café in Michigan City where I spent various hours last winter writing this book
withadeliciouscappuccinobesideme;ormystudyroominFisherApartmentson
Notre Dame Campus, looking out the window at a squirrel holding a nut; or on a
narrow tray table while taking an Inter-Regio train between Berlin and Hamburg,
which was where I drew a diagram using a pen and probably a napkin; and of
course,mydelightfulofficeattheCatholicUniversityofChilewithitsbreathtaking
view of the Andes Mountains.
Thisbookhasbeenputtogetheronthebasisoffourmainpillarsthathavebeen
constructedoverthelast15years:thefirstpillaristhesetofjournalandconference
papers that I have published. The second corresponds to the material used in my
ix
x Preface
classes and the feedback received from students when I have been teaching image
processing,patternrecognition,andcomputer vision.Thethirdpillar istheMatlab
Toolbox that I was able to develop during this time, and which has been tested in
severalexperiments,classes,andresearchprojects,amongothers.Thefourthpillar
is the thousands of X-ray images that my research group has been taking in recent
yearsatourLab,andtheX-rayimagesofdiecastingsthatItookinHamburg.Over
allthistime,Ihaverealizedthatthisamountofworkcanallbebroughttogetherin
a book that collects the most important contributions in computer vision used in
X-ray testing.
Scope
X-ray imaging has been developed not only for its use in medical imaging for
humans, but also for materials or objects, where the aim is to analyze—nonde-
structively—those inner parts that are undetectable to the naked eye. Thus, X-ray
testing is used to determine if a test object deviates from a given set of specifica-
tions. Typical applications are analysis of food products, screening of baggage,
inspection of automotive parts, and quality control of welds. In order to achieve
efficient and effective X-ray testing, automated and semi-automated systems are
beingdevelopedtoexecutethistask.Inthisbook,wepresentageneraloverviewof
computer vision methodologies that have been used in X-ray testing. In addition,
some techniques that have been applied in certain relevant applications are pre-
sented: there are also some areas—like casting inspection—where automated sys-
tems are very effective, and other application areas—such as baggage screening—
where human inspection is still used. There are certain application areas—like
welds and cargo inspections—where the process is semi-automatic; and there is
someresearchinareas—includingfoodanalysis—whereprocessesarebeginningto
be characterized by the use of X-ray imaging. In this book, Matlab programs for
image analysis and computer vision algorithms are presented with real X-ray
images that are available in a public database created for testing and evaluation.
Organization
The book is organized as follows:
Chapter 1 (X-ray Testing): This chapter provides an introduction to the book.
ItillustratesprinciplesaboutthephysicsofX-rays,anddescribesX-raytestingand
imaging systems, while also summarizing the most important issues on computer
vision for X-ray testing.
Chapter2(ImagesforX-rayTesting):Thischapterpresentsadescriptionofthe
GDXraydatabase,thedatasetofmorethan19,400X-rayimagesusedinthisbook
Preface xi
to illustrate and test several computer vision methods. The database includes five
groups of X-ray images: castings, welds, baggage, natural objects and settings.
Chapter 3 (Geometry in X-ray Testing): This chapter presents a mathematical
backgroundofthemonocular andmultiple view geometrythat isnormallyusedin
X-ray computer vision systems.
Chapter4(X-rayImageProcessing):Thissectioncoversthemaintechniquesof
image processing used in X-ray testing, such as image pre-processing, image fil-
tering, edge detection, image segmentation, and image restoration.
Chapter5(X-rayImageRepresentation):Thischaptercoversseveraltopicsthat
areusedtorepresentanX-rayimage(oraspecificregionofanX-rayimage).This
representation means that new features are extracted from the original image; this
can provide us with more data than the raw information expressed as a matrix of
gray values.
Chapter 6 (Classification in X-ray Testing): This section covers known classi-
fiers with several examples that can be easily modified in order to test different
classification strategies. Additionally, the chapter covers how to estimate the
accuracy of a classifier using hold-out, cross-validation and leave-one-out
approaches.
Chapter 7 (Simulation in X-ray Testing): This chapter reviews some basic
concepts of the simulation of X-ray images, and presents simple geometric and
imaging models that can be used in the simulation.
Chapter 8 (Applications in X-ray Testing): This section describes relevant
applicationsforX-raytestingsuchastheinspectionofcastingsandwelds,baggage
screening, quality control of natural products, and inspection of cargos and elec-
tronic circuits.
Who Is This Book For
Thisbookcoversanintroductiontocomputervisionalgorithmsthatcanbeusedin
X-raytestingproblemssuchasdefectdetection,baggagescreening,3Drecognition,
quality control of food products, and inspection of cargos and electronic circuits,
among others. This work may not be ideal for students of computer science or
electrical engineering who want to obtain a deeper knowledge of computer vision
(for which purpose there are many wonderful textbooks on image processing,
pattern recognition, and computer vision1). Rather, it is a good starting point for
undergraduateorgraduatestudentswhowishtolearnbasiccomputervisionandits
application in problems of industrial radiology.2 Thus, the aim of this book is
to cover complex topics on computer vision in an easy and accessible way.
1Seeforexample[1–8].
2Obviously,thealgorithmsoutlinedinthisbookcanbeusedinsimilarapplicationssuchasglass
inspection[9]orqualitycontroloffoodproductsusingopticalimages[10]—tonamebutafew.