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2017 | Buch

Metaheuristics for Medicine and Biology

herausgegeben von: Amir Nakib, El-Ghazali Talbi

Verlag: Springer Berlin Heidelberg

Buchreihe : Studies in Computational Intelligence

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Über dieses Buch

This book highlights recent research on metaheuristics for biomedical engineering, addressing both theoretical and applications aspects. Given the multidisciplinary nature of bio-medical image analysis, it has now become one of the most central topics in computer science, computer engineering and electrical and electronic engineering, and attracted the interest of many researchers.

To deal with these problems, many traditional and recent methods, algorithms and techniques have been proposed. Among them, metaheuristics is the most common choice. This book provides essential content for senior and young researchers interested in methodologies for implementing metaheuristics to help solve biomedical engineering problems.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Design of Static Metaheuristics for Medical Image Analysis
Abstract
Medical images, such as Computed Axial Tomography (CAT), Magnetic Resonance Imaging (MRI), Ultrasound, and X-Ray, in standard DICOM (Digital Imaging and Communications in Medicine) formats are often stored in Picture Archiving and Communication Systems (PACS) and linked with other clinical information in clinical management systems.
Amir Nakib
Chapter 2. Multi-level Image Thresholding Based on Hybrid Differential Evolution Algorithm. Application on Medical Images
Abstract
Image thresholding is definitely one of the most popular segmentation approaches for extracting objects from the background, or for discriminating objects from objects that have distinct gray-levels. It is typically simple and computationally efficient. It is based on the assumption that the objects can be distinguished by their gray levels.
M. Ali, P. Siarry, M. Pant
Chapter 3. Fuzzy Edge Detection in Computed Tomography Through Genetic Algorithm Optimization
Abstract
The ill posedness of the image reconstruction problem requires approached solution as a regularization of a specific criterion, in general, a penalty is imposed on the solution. The challenge is to avoid the smoothing of edges which are very important attributes of the image when it is regularized. The x-ray Tomography is classified as sensing problems for which we do not know the equipment measurement transfer function so it is considered as an ill posed inverse problem. Many studies have been developed to solve this problem, among them the Bayesian inference which aims at smoothing artifact in image. The problem for Bayesian methods is the edge penalization. In this work, we first present a fuzzy inference model for the edge preservation. Under this condition, we show that it is possible to find the best global solution to the problem by introducing genetic algorithm optimization (GA).
A. M. T. Gouicem, M. Yahi, A. Taleb-Ahmed
Chapter 4. Particle Swarm Optimization Based Fast Chan-Vese Algorithm for Medical Image Segmentation
Abstract
Image segmentation is a very important part of image pre-processing and its application towards computer vision.
Devraj Mandal, Amitava Chatterjee, Madhubanti Maitra
Chapter 5. Evidential Deformable Model for Contour Tracking. Application on Brain Cine MR Sequences
Abstract
The goal of this paper is to introduce an efficient evidential particle filter for complex shapes tracking. The particularity of that particle filter is not only the fair use of the observation at the current time in the update step of it by performing a curve evolution but also it represents a bridge between Probability function and Evidence theory. This bridge can be illustrated by incorporating a data fusion step in the update phase. This method builds a track by selecting the best particles between the particle candidates. This re-sampling phase is based on choosing the particles possessing the higher value of the basic belief assignment function. The values of these basic belief assignment functions are resulting from the fusion process of two distinctive sources of information. The first source is the energy functional and the second one is the local sensitive histogram. The evaluation of our approach, which is made on a realistic Brain cine RM sequences, aims at tracking the motion of the walls of the third ventricle. Therefore, the latter shows its obvious and clear efficiency. In order to validate our proposal, we present a comparative study between our proposal and the state of the art methods. The obtained results are encouraging.
Sarra Naffakhi, Amir Nakib, Atef Hamouda
Chapter 6. Microscopic Image Segmentation Based on Based Branch and Bound and Game Theory
Abstract
In this work a new family of image segmentation algorithms is proposed. This paper is a generalization of the model proposed, called: Power Watershed segmentation framework. Indeed, we extended it for cases: \(2< q < \inf \) and \(p \rightarrow \inf \). To do so, we explore the segmentation a new formulation of the segmentation problem based on game theory is proposed optimization energy function as a game theory problem. In this new formulation, The minimization can be, then, optimization process is seen as a search of the Nash equilibrium of a non-cooperative strategic game. Indeed, the computation of Nash equilibrium in finite game is equivalent to a non linear optimization problem afterward. As the optimization problem thus formulated the computation of the Nash equilibrium is an NP-hard problem, then, we propose the use of the Branch and Bound method is used to solve it to find it in reasonable time. In this study moreover, the uniqueness of the Nash equilibrium is demonstrated using a potential game-theoretic approach. Then we propose a new family of segmentation approach with \(2< q < \inf \)and \(p \rightarrow \inf \), named Game-based PW. The obtained results of the proposed approach, show are better than those given by the original Power Watershed \(q = 2\).
Amira Kouzana, Amir Nakib, Narjes Dogaz
Chapter 7. Dynamic Metaheuristics for Brain Cine-MRI
Abstract
Recently, a new technique for obtaining brain images of cine-MR (Magnetic Resonance) type has been developed by Hodel et al., (Brain ventricular wall movement assessed by a gated cine MR true FISP sequence in patients treated with endoscopic third ventriculostomy 19(12), (2009), [8]). The principle of this technique is to synchronize the MRI signal with the ECG (Electrocardiographic) signal. The MRI signal provides three dimensional images and cuts of high anatomical precision, and the ECG signal is obtained from the heart activity.
Amir Nakib
Chapter 8. Lexicographic Approach Based on Evidence Theory for Blood Cell Image Segmentation
Abstract
The analysis of microscope cell blood images can provide useful information concerning health of patients; the main different components of blood are White Blood Cells (WBCs), Red Blood Cells (RBCs) and platelets. When a disease and foreign materials infect human bodies, the number of WBCs increases to respond and defend infection.
Ismahan Baghli, Amir Nakib
Chapter 9. Medical Image Denoising Using Metaheuristics
Abstract
In recent years, metaheuristic optimization techniques have attracted much attention from researchers and practitioners and they have been widely used to solve complex or specific optimization problems in all fields, from engineering area to finance [2].
Serdar Kockanat, Nurhan Karaboga
Chapter 10. Medical Image Registration Based on Metaheuristics: A Comparative Study
Abstract
Image registration is the process of overlaying two or more images of the same scene taken at different times, from different viewpoints, and/or by different sensors. It is a critical step in all image analysis tasks in which the final information is gained from the combination of various data sources like in image fusion or change detection.
A. Nakib, E.-G. Talbi, S. Corniglion
Chapter 11. Adaptive ECG Signal Filtering Using Bayesian Based Evolutionary Algorithm
Abstract
Metaheuristics have been widely used to solve many different optimization problems, however, when the dimension of the problems increases the performance of theses algorithms decreases. This decrease of the performance limited the use of this approach, when the dimension is high, these problems are large scale problems. Many authors proposed several approaches to enhance the performance of the algorithms. The reader can see recent review papers as.
Thibaut Bernard, Amir Nakib
Metadaten
Titel
Metaheuristics for Medicine and Biology
herausgegeben von
Amir Nakib
El-Ghazali Talbi
Copyright-Jahr
2017
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-54428-0
Print ISBN
978-3-662-54426-6
DOI
https://doi.org/10.1007/978-3-662-54428-0