Metaheuristics for Medicine and Biology

221 Pages·2017·10.999 MB·English

by  Amir Nakib, El-Ghazali Talbi (eds.)

Studies in Computational Intelligence 704
Amir Nakib
El-Ghazali Talbi E ditors
Metaheuristics
for Medicine
and Biology
Studies in Computational Intelligence
Volume 704
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
About this Series
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the worldwide distribution,
which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
Amir Nakib El-Ghazali Talbi
(cid:129)
Editors
Metaheuristics for Medicine
and Biology
123
Editors
Amir Nakib El-Ghazali Talbi
Laboratoire Images,Signaux etSystèmes INRIA LilleNord Europe ParcScientifique
Intelligents dela Haute Borne
UniversitéParis EstCréteil Laboratoired’InformatiqueFondamentalede
Créteil Lille(UMR CNRS 8022)
France Villeneuve d’Ascq
France
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-662-54426-6 ISBN978-3-662-54428-0 (eBook)
DOI 10.1007/978-3-662-54428-0
LibraryofCongressControlNumber:2017933440
©Springer-VerlagGmbHGermany2017
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart
of 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 transmission
orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar
methodologynowknownorhereafterdeveloped.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom
therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
The publisher, the authors and the editors are safe to assume that the advice and information in this
book are believed to be true and accurate at the date of publication. Neither the publisher nor the
authorsortheeditorsgiveawarranty,expressorimplied,withrespecttothematerialcontainedhereinor
for any errors or omissions that may have been made. The publisher remains neutral with regard to
jurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations.
Printedonacid-freepaper
ThisSpringerimprintispublishedbySpringerNature
TheregisteredcompanyisSpringer-VerlagGmbHGermany
Theregisteredcompanyaddressis:HeidelbergerPlatz3,14197Berlin,Germany
To my parents (Amir Nakib)
Preface
The metaheuristics appeared in the eighties. These global optimization algorithms
arestochasticandcanbeappliedtoanyproblem,attheconditionitisformulatedas
a mono-objective or multiobjective optimization problem. They are called nature
inspired because their origin comes from the observation of natural behavior:
analogy with physics (simulated annealing, microcanonical annealing), with biol-
ogy (evolutionary algorithms) or with ethology (ant colonies, particle swarms).
Theyalsocanbeextended,particularlytomultiobjectiveoptimization.Algorithms,
techniques, and methods based on metaheuristic paradigm have been successfully
applied to a wide range of complex problems. From the perspective of science
development, metaheuristics is an emerging interdisciplinary area between natural
sciences, biology, sociology, and computer science. Its rapid growth is a natural
product of the rapid development of interdisciplinary research today.
Medical imaging has established itself as a very important research area pri-
marily due to the rapid development of sensors, communication technologies,
databases, processors, etc. The phenomenal growth in the technologies and appli-
cations for medical imaging has allowed for many interesting results concerning,
Medical image acquisition, Medical image processing, and Telemedicine.
This book “Metaheuristics for Medicine and Biology” aims at providing a
reviewforresearchersinterestedintheadvancesandapplicationsofmetaheuristics
to biomedical engineering. The book is oriented towards both theoretical and
applications aspects of metaheuristics to biomedical imaging.
Paris Amir Nakib
December 2016 El-Ghazali Talbi
vii
Acknowledgements
The Guest Editor, would like to thank:
(cid:129) Dr. R. Blanc, co-head of the Department of Interventional Neuroradiology,
Fondation A. De Rothschild, Paris, France.
(cid:129) Pr. P. Decq Head of the Department of Neurosurgery at CHU Henri Mondor,
Créteil, France.
ix
Contents
1 Design of Static Metaheuristics for Medical Image Analysis.... .. 1
Amir Nakib
1.1 Introduction .... .... ..... .... .... .... .... .... ..... .. 1
1.2 Image Segmentation as an Optimization Problem .... ..... .. 2
1.3 Metaheuristics’ Enhancement for Medical Image
Segmentation... .... ..... .... .... .... .... .... ..... .. 3
1.3.1 Hybrid Ant Colony System (ACS). .... .... ..... .. 3
1.3.2 Enhanced BBO for Image Segmentation. .... ..... .. 8
1.3.3 Enhanced DE for Image Thresholding .. .... ..... .. 13
1.3.4 Metaheuristic for Contours Detection
in 2D Ultrasound Images .... .... .... .... ..... .. 16
1.4 Conclusion. .... .... ..... .... .... .... .... .... ..... .. 21
References .. .... .... .... ..... .... .... .... .... .... ..... .. 21
2 Multi-level Image Thresholding Based on Hybrid Differential
Evolution Algorithm. Application on Medical Images.... ..... .. 23
M. Ali, P. Siarry and M. Pant
2.1 Introduction .... .... ..... .... .... .... .... .... ..... .. 23
2.2 Gaussian Curve Fitting..... .... .... .... .... .... ..... .. 24
2.3 Overall Probability of Error. .... .... .... .... .... ..... .. 25
2.4 Hybrid Differential Evolution (HDE).. .... .... .... ..... .. 26
2.5 Experimental Results . ..... .... .... .... .... .... ..... .. 28
2.6 MRI Slices Segmentation... .... .... .... .... .... ..... .. 32
2.7 Conclusions.... .... ..... .... .... .... .... .... ..... .. 35
References .. .... .... .... ..... .... .... .... .... .... ..... .. 35
3 Fuzzy Edge Detection in Computed Tomography
Through Genetic Algorithm Optimization . .... .... .... ..... .. 37
A.M.T. Gouicem, M. Yahi and A. Taleb-Ahmed
3.1 Introduction .... .... ..... .... .... .... .... .... ..... .. 37
xi
xii Contents
3.1.1 Problem Statement . .... .... .... .... .... ..... .. 38
3.2 Applied Methods.... ..... .... .... .... .... .... ..... .. 39
3.2.1 Implementation of Fuzzy Inference. .... .... ..... .. 40
3.2.2 Genetic Algorithm (GA). .... .... .... .... ..... .. 41
3.2.3 Optimization by Genetic Algorithm .... .... ..... .. 43
3.3 Results and Discussion..... .... .... .... .... .... ..... .. 44
3.4 Conclusion. .... .... ..... .... .... .... .... .... ..... .. 46
References .. .... .... .... ..... .... .... .... .... .... ..... .. 47
4 Particle Swarm Optimization Based Fast Chan-Vese
Algorithm for Medical Image Segmentation.... .... .... ..... .. 49
Devraj Mandal, Amitava Chatterjee and Madhubanti Maitra
4.1 Introduction .... .... ..... .... .... .... .... .... ..... .. 49
4.2 The C-V Model (Piecewise-Constant Model) for Image
Segmentation... .... ..... .... .... .... .... .... ..... .. 51
4.2.1 Level Set Formulation of the Model.... .... ..... .. 52
4.2.2 The C-V Algorithm .... .... .... .... .... ..... .. 53
4.2.3 The Strengths and Drawbacks of the C-V
Algorithm... ..... .... .... .... .... .... ..... .. 53
4.2.4 Extension of C-V Model for Vector-Valued Images. .. 55
4.2.5 Extension of C-V Model for Multi-phase Level
Set Implementation. .... .... .... .... .... ..... .. 56
4.3 Evolutionary Formulation of Our Fast C-V Model.... ..... .. 58
4.4 Particle Swarm Optimization (PSO) Based Fast C-V Model . .. 58
4.5 Implementation and Results. .... .... .... .... .... ..... .. 62
4.5.1 Two-Class Implementation for Scalar Images. ..... .. 62
4.5.2 Two-Class Implementation for Vector Valued
Images . .... ..... .... .... .... .... .... ..... .. 65
4.5.3 Multi-class Implementation of Our Proposed
Model.. .... ..... .... .... .... .... .... ..... .. 66
4.6 Conclusions.... .... ..... .... .... .... .... .... ..... .. 71
References .. .... .... .... ..... .... .... .... .... .... ..... .. 72
5 Evidential Deformable Model for Contour Tracking.
Application on Brain Cine MR Sequences . .... .... .... ..... .. 75
Sarra Naffakhi, Amir Nakib and Atef Hamouda
5.1 Introduction .... .... ..... .... .... .... .... .... ..... .. 75
5.2 Related Works.. .... ..... .... .... .... .... .... ..... .. 76
5.3 The Evidence Theory. ..... .... .... .... .... .... ..... .. 78
5.4 Proposed Method.... ..... .... .... .... .... .... ..... .. 78
5.4.1 Description.. ..... .... .... .... .... .... ..... .. 79
5.4.2 Evidential Modeling.... .... .... .... .... ..... .. 80
5.4.3 Combination Rule from Each Source ... .... ..... .. 81