A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis

[Display omitted] •Metaheuristics (MHs) are general-purpose stochastic optimization methods.•A Deformable Model (DM) tries to maximize its overlap with the object to segment.•This survey is the first review about the hybridization of DMs and MHs.•We provide guidelines to choose/design your hybrid se...

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Vydáno v:Applied soft computing Ročník 44; s. 1 - 29
Hlavní autoři: Mesejo, Pablo, Ibáñez, Óscar, Cordón, Óscar, Cagnoni, Stefano
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.07.2016
Elsevier
Témata:
Artificial Intelligence
Computer Science
Computer Vision and Pattern Recognition
Deformable models
Global continuous optimization
Image Processing
Image segmentation
Medical Imaging
Metaheuristics
Signal and Image Processing
Stochastic optimization
Deformable models
Image segmentation
Global continuous optimization
Stochastic optimization
Metaheuristics
Active Shape Models
Global Continuous Optimization
Topological Active Nets
Scatter Search
Level Set method
Image Segmentation
Differential Evolution
Stochastic Optimization
Deformable Models
Genetic Algorithms
Particle Swarm Optimization
ISSN:1568-4946, 1872-9681
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Abstract [Display omitted] •Metaheuristics (MHs) are general-purpose stochastic optimization methods.•A Deformable Model (DM) tries to maximize its overlap with the object to segment.•This survey is the first review about the hybridization of DMs and MHs.•We provide guidelines to choose/design your hybrid segmentation approach.•This review paper studies, analyzes and contextualizes more than 120 papers.•MHs help in parameters selection, initial boundary location and DM contour evolution. Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires the definition of an optimization framework whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecting the optimization and segmentation algorithms’ parameters; choosing the representation for encoding prior knowledge on the image domain of interest; and initializing the curve in a location which favors its convergence onto the boundary of the object of interest. All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem. After recalling the principles underlying deformable models and metaheuristics, we broadly review the different hybrid approaches employed to solve image segmentation problems, and conclude with a general discussion about methodological and design issues as well as future research and application trends.
AbstractList Deformable models are segmentation techniques that adapt a curve to maximize its overlap with the actual contour of an object of interest within an image. Such a process requires the definition of an optimization framework whose most critical issues include choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces, selecting the optimization and segmentation algorithms’parameters, choosing the representation for encoding prior knowledge on the image domain of interest, and initializing the curve in a location which favors its convergence onto the boundary of the object of interest.All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques generally termed metaheuristics, designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem.After recalling the principles underlying deformable models and metaheuristics, we broadly review the different metaheuristic-based approaches to image segmentation based on deformable models, and conclude with a general discussion about methodological and design issues as well as future research and applications trends.
[Display omitted] •Metaheuristics (MHs) are general-purpose stochastic optimization methods.•A Deformable Model (DM) tries to maximize its overlap with the object to segment.•This survey is the first review about the hybridization of DMs and MHs.•We provide guidelines to choose/design your hybrid segmentation approach.•This review paper studies, analyzes and contextualizes more than 120 papers.•MHs help in parameters selection, initial boundary location and DM contour evolution. Deformable models are segmentation techniques that adapt a curve with the goal of maximizing its overlap with the actual contour of an object of interest within an image. Such a process requires the definition of an optimization framework whose most critical issues include: choosing an optimization method which exhibits robustness with respect to noisy and highly-multimodal search spaces; selecting the optimization and segmentation algorithms’ parameters; choosing the representation for encoding prior knowledge on the image domain of interest; and initializing the curve in a location which favors its convergence onto the boundary of the object of interest. All these problems are extensively discussed within this manuscript, with reference to the family of global stochastic optimization techniques that are generally termed metaheuristics, and are designed to solve complex optimization and machine learning problems. In particular, we present a complete study on the application of metaheuristics to image segmentation based on deformable models. This survey studies, analyzes and contextualizes the most notable and recent works on this topic, proposing an original categorization for these hybrid approaches. It aims to serve as a reference work which proposes some guidelines for choosing and designing the most appropriate combination of deformable models and metaheuristics when facing a given segmentation problem. After recalling the principles underlying deformable models and metaheuristics, we broadly review the different hybrid approaches employed to solve image segmentation problems, and conclude with a general discussion about methodological and design issues as well as future research and application trends.
Author Ibáñez, Óscar
Cagnoni, Stefano
Mesejo, Pablo
Cordón, Óscar
Author_xml – sequence: 1
  givenname: Pablo
  surname: Mesejo
  fullname: Mesejo, Pablo
  email: pmesejo@ce.unipr.it
  organization: Department of Information Engineering, University of Parma, Viale G.P. Usberti 181a, 43124 Parma, Italy
– sequence: 2
  givenname: Óscar
  orcidid: 0000-0002-5301-8277
  surname: Ibáñez
  fullname: Ibáñez, Óscar
  email: oscar.ibanez@decsai.ugr.es
  organization: Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda, 18071 Granada, Spain
– sequence: 3
  givenname: Óscar
  surname: Cordón
  fullname: Cordón, Óscar
  email: ocordon@decsai.ugr.es
  organization: Department of Computer Science and Artificial Intelligence, University of Granada, C/ Daniel Saucedo Aranda, 18071 Granada, Spain
– sequence: 4
  givenname: Stefano
  surname: Cagnoni
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Copyright 2016 Elsevier B.V.
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Keywords Deformable models
Image segmentation
Global continuous optimization
Stochastic optimization
Metaheuristics
Active Shape Models
Global Continuous Optimization
Topological Active Nets
Scatter Search
Level Set method
Image Segmentation
Differential Evolution
Stochastic Optimization
Deformable Models
Genetic Algorithms
Particle Swarm Optimization
Language English
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Elsevier
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Snippet [Display omitted] •Metaheuristics (MHs) are general-purpose stochastic optimization methods.•A Deformable Model (DM) tries to maximize its overlap with the...
Deformable models are segmentation techniques that adapt a curve to maximize its overlap with the actual contour of an object of interest within an image. Such...
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SubjectTerms Artificial Intelligence
Computer Science
Computer Vision and Pattern Recognition
Deformable models
Global continuous optimization
Image Processing
Image segmentation
Medical Imaging
Metaheuristics
Signal and Image Processing
Stochastic optimization
Title A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis
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