Guide to Medical Image Analysis_ Methods and Algorithms

The methodology presented in the first edition was considered established practiceor settled science in the medical image analysis community in 2010–2011. Progressin this field is fast (as in all fields of computer science) with several developmentsbeing particularly relevant to subjects treated Moreinformationaboutthisseriesathttp://www.springer.com/series/4205Klaus d. toenniesGuide to Medical ImageAnalysisMethods and algorithmsSecond editionSpringerKlaus d. toenniesComputer Science department ISGOtto-von-Guericke-Universitat MagdeburgMagdeburgGermanyThe author() has/have asserted their right(s) to be identified as the author(s)of this work inaccordance with the Copyright, Design and Patents Act 1988ISSN2191-6586issN 2191-6594(electronic)Advances in Computer Vision and Pattern RecognitionISBN978-1-4471-7318-2ISBN978-1-4471-7320-5( e Book)DOⅠ10.1007/978-1-4471-7320-5Library of Congress Control Number: 2017932114o Springer-Verlag London Ltd. 2012. 2017This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developedThe use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general useThe publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliationsPrinted on acid-free paperThis Springer imprint is published by Springer NatureThe registered company is Springer-Verlag London LtdThe registered company address is: 236 Gray's Inn Road, London WCIX 8HB, United KingdomPreface to the 2nd editionThe methodology presented in the first edition was considered established practiceor settled science in the medical image analysis community in 2010-2011. Progressn this field is fast(as in all fields of computer science) with several developmentsbeing particularly relevant to subjects treated in this bookImage-based guidance in the operating room is no longer restricted to the display of planning images during intervention. It is increasingly meant to aid theoperator to adapt his or her intervention technique during operation. Thisrequires reliable and intuitive analysis methodsSegmentation and labeling of images is now mostly treated as solution of anoptimization problem in the discrete(Chap. 8)or in the continuous domain(Chap 9). Heuristic methods such as the one presented in Chap 6 still exist innon-commercial and commercial software products, but searching for resultsthat optimize an assumption about how the information is mapped to the dataproduces more predictable methodsDeep learning gives new impulses to many areas in medical image analysis as itcombines learning of features from data with the abstraction ability of multilayerperceptrons. Hence, learning strategies can be applied directly to pixels in alabeling task. It promises analysis methods that are not designed for a specificproblem but can be trained from examples in this problem domainBesides actualizing all chapters and removing typos from the first edition, wefocused on methods that relate to these three points in the new edition. The generalstructure of the book has not been changed which also means that the different usesof the book for courses on medical image analysis suggested in the preface for thefirst edition remain valid. However the focus of the book on segmentation classification, and registration has been strengthened further. These tasks are particularly important if image guidance in the operation room is needed since it requireswell-understood and reliable tools to extract information from the image dataIn Chaps. 4 and 5, we added established methods for feature generation andedge-preserving smoothing. Feature generation, i.e., the reduction and transfor-mation of image data to a small set of features, is important for efficient and fastextraction of information from images. Edge-preserving smoothing reduces thenoise, which is inherent to most imaging techniques, while keeping edges as majorPreface to the 2nd editioncontributors to feature recognition intact. It is a difficult problem, since noise andother artefacts have to be differentiated from relevant edge features prior todetermining just these edgesIn the Chaps. 8 and 9, we added methods to include a priori knowledge intograph-based and level set-based segmentation methods. The basic methods in thetwo chapters represent two strategies to solve segmentation and labeling problemsas optimization tasks by minimizing the total variation of an energy functional andhave been adopted in many solutions in medical image analysis. In the extendedtreatment, we present different local characteristics of information and artefacts andhow they can be included in the respective energy functional. We also presentmethods in Chap. ll how to integrate high-level knowledge about shape, appear-ance, and expected position of searched objects into the two frameworks presentedin Chaps 8 and gIn Chap 10, we extended the treatment of non-rigid registration and discuss jointsegmentation and registration with more depth. As image-guided interventionplanning often requires to register organs that move differently with respect to eachother, we added a section on locally varying regularizers to model a sliding motionbetween organsFinally, we added a section in Chap. 12 about using deep convolutional networks that describes how this network architecture differs from multilayer per-ceptrons (MLps)and how these networks can be used for segmentation andlabeling tasks by adding a feature detection and reduction stage to an MLP.Magdeburg, GermanyKlaus d. toenniesAcknowledgementsSeveral persons supported me in writing this new edition. I wish to thank my Ph. Dstudents Georg Hille, Tim KOnig, Marko Rak, and Johannes Steffen who read andcorrected part of the chapters. They surely helped to clarify the presentation of thebook. Johannes steffen also contributed to the section on deep learning to make itmore concise.i also wish to thank dr laura astola who used this book in hercourse and gave me an embarrassingly long list of all the errors that i produced irthe first edition. I corrected them all and hopefully produced not too many newtypos. Finally, I wish to thank Stefanie Quade for proofreading and correcting thetext. I learned a lot about the english language from her, and it certainly improvedthe readability of the bookPreface to the 1st EditionHans Castorp in Thomas Manns Magic Mountain, keeps an X-ray of his love as itseems to him the most intimate of her to possess. Professionals will think differentof medical images, but the fascination with the ability to see the unseeable issimilar. And, of course, it is no longer just X-ray. Today, it is not sparseness butwealth and diversity of the many different methods to generate images of the humanbody that make understanding of the depicted content difficult. At any point in timein the last twenty years, at least one or two ways to acquire a new kind of imagehave been in the pipeline from research to development and application. Currentlyoptical coherence tomography and MEg are among those somewhere betweendevelopment and first clinical application. At the same time, established techniquessuch as ct or Mri reach new heights with respect to depicted content, imagequality, or speed of acquisition, opening them to new fields in the medical sciencesmages are not self-explanatory, however. Their interpretation requires professional skill that has to grow with the number of different imaging techniques. Themany case reports and scientific articles about the use of images in diagnosis andtherapy bear witness to this. Since the appearance of digital images in the 1970s,information technologies have had a part in this. The task of computer science hasbeen and still is the quantification of information in the images by supportingdetection and delineation of structures from an image or from the fusion of information from different image sources. While certainly not having the elaborate skillsof a trained professional, automatic or semi-automatic analysis algorithms have theadvantage of repeatedly performing tasks of image analysis with constant qualityhence relieving the human operator from the tedious and fatiguing parts of theInterpretation task.By the standards of computer science, computer-based image analysis is an oldresearch field with first applications in the 1960s Images in general are such afascinating subject because the data elements contain so little information, while thewhole image captures such a wide range of semantics. Just take a picture from yourlast vacation and look for information in it. It is not just Uncle Harry but also thebeauty of the background, the weather and time of the day, the geographicallocation and many other kinds of information that can be gained from a collectionof pixels of which the only information is intensity, hue, and saturationVIllPreface to the 1 st editionConsequently, a wealth of methods has been developed to integrate the necessaryknowledge in an interpretation algorithm for arriving at this kind of semanticsAlthough medical images differ from photography in many aspects, similartechniques of image analysis can be applied to extract meaning from medicalimages. Moreover, the profit from applying image analysis in a medical applicationis immediately visible, as it saves times or increases reliability of an interpretationtask needed to carry out a necessary medical procedure. It requires however, thatthe method is selected adequately, applied correctly, and validated sufficientlyThis book originates from lectures about the processing and analysis of medicalimages for students in computer science and computational visualistics who want tospecialize in medical imaging. The topics discussed in the lectures have beenrearranged in order to provide a single comprehensive view on the subject. Thebook is structured according to the potential applications in medical image analysisIt is a different perspective if compared to image analysis, where usually abottom-up sequence from pixel information to image content is preferred. Whereverit was possible to follow the traditional structure, this has been done. However, ifthe methodological perspective conflicted with the view from an application perspective, the latter has been chosen. The most notable difference is in the treatmentof classification and clustering techniques that appears twice, since differentmethods are suitable for segmentation in low-dimensional feature space comparedto classification in high-dimensional feature spaceThe book is intended to be used for medical professionals who want to getacquainted with image analysis techniques, for professionals in medical imagintechnology, and for computer scientists and electrical engineers who want to specialize in the medical applications. A medical professional may want to skip thesecond chapter as he or she will be more intimately acquainted with medical imagesthan the introduction in this chapter can provide. It may be necessary to acquiresome additional background knowledge in image or signal processing. However,only the most basic material was omitted(e. g, definition of the Fourier transformand convolution), information about which is freely available on the Internet. Anengineer, on the other hand, may want to get more insight into the clinical work-flow, in which analysis algorithms are integrated. The topic is presented briefly inthis book but a much better understanding is gained from collaboration withmedical professionals a beautiful algorithmic solution can be virtually useless ifconstraints from the application are not adhered toAs it was developed from course material, the book is intended to be used inlectures on the processing and analysis of medical images. There are several pos-sibilities to use subsets of the book for single courses which can be combinedThree of the possibilities that I have tried myself are listed below(Cx refers to thchapter number)Medical Image generation and Processing(Bachelor course supplemented with exercises to use MATLAB or anothertoolbox for carrying out image processing tasks)Preface to the 1 st editionC2: Imaging techniques in detail (4 lecturesC3: DICOM (I lecture)C4: Image enhancement(2 lectures)C5: Feature generation(I lecture)C6: Basic segmentation techniques(2 lecturesC12: Classification(1 lecture)C13: Validation(I lectureIntroduction to Medical Image processing and analysis(Bachelor course supplemented with a students project to solve a moderatelychallenging image analysis task; requires background on imaging techniques ):C2: Review of major digital imaging techniques: X-ray, CT, Mri, ultrasound, and nuclear imaging(1 lecture)C3: Information systems in hospitals(I lecture)C4: Image enhancement (1 lectureC6: Basic segmentation techniques(2 lectures)C7: Segmentation as a classification task(I lecture)C8-C9: Introduction to graph cuts, active contours, and level sets(2 lectures,C10: Rigid and non-rigid registration(2 lectures)Cll: Active shape model (I lecture)C13: Validation(1 lecture)Advanced Image analysis(Master course supplemented with a seminar on hot topics in this field)C7: Segmentation by using mres(l lecturesC8: Segmentation as operation on graphs(3 lecturesC9: Active contours, active surfaces, level sets (4 lectures)Cll: Object detection with shape(4 lectures)Most subjects are presented so that they can also be read on a cursory levelomitting derivations and details. This is intentional to allow a reader to understanddependencies of a subject on other subjects without having to go into detail in eachone of them. It should also help to teach medical image analysis on the level of aBachelor’ s course..Medical image analysis is a rewarding field for investigating, developing, andapplying methods of image processing, computer vision, and pattern recognitionI hope that this book gives the reader a sense of the breadth of this area and its manychallenges while providing him or her with the basic tools to take the challengemagdeburg, GermanyKlaus d. toennies