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A Hybrid Segmentation Approach of Brain Magnetic Resonance Imaging Using Region-Based Active Contour with a Similarity Factor and Multi-Population Genetic Algorithm

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Abstract

The performance of medical image segmentation is generally affected by the parameters of the adopted method and noise. To overcome these issues we introduce in this paper a novel segmentation approach of brain MRI using a region based-active contour model and evolutionary algorithm and without performing any pre-processing step. Our main objective is to accurately extract edges, resolve the intensity inhomogeneity problem and overcome manifestations of noise. Chan and Vese model was adopted by introducing a local similarity factor based on Bilateral filter principle (LSFB). The adjustment of our functional energy parameters was achieved using a multi-population genetic algorithm (MPGA) which can display better search performance than serial single population models, in terms of the quality of the solution found, effort and processing time. We selected Brain MRI from Oasis and Brainweb data base with different noise type. The initialization of the active contour was totally random. A comparison of segmentation results with Chan and Vese model and active contour model with a locally computed signed pressure force (SPF) of Akram and his team reveals a clear efficiency of our proposed approach.

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Authors and Affiliations

  1. Département de Mathématiques et Informatique, Institut des Sciences, Centre UniversitaireBelhadjBouchaib -Ain Témouchent, CUBBAT, route de sidi bel abess, N101, 46000, Ain Temouchent, Algerie

    Fatima Zohra Belgrana

  2. Laboratoire SIMPA, Départementd’Informatique, Faculté des Mathématiques et d’Informatique, Université des Sciences et de la Technologied’Oran Mohamed Boudiaf, USTO-MB, BP 1505, EL Mnaouer, 31000, Oran, Algérie

    Fatima Zohra Belgrana, Nacéra Benamrane & Sid Ahmed Kasmi

Authors
  1. Fatima Zohra Belgrana
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  2. Nacéra Benamrane
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  3. Sid Ahmed Kasmi
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Correspondence to Fatima Zohra Belgrana, Nacéra Benamrane or Sid Ahmed Kasmi.

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We would like to thank everybody who has contributed to this work.

The authors declare that they have no conflicts of interest.

This article does not contain any studies involving animals performed by any of the authors.

This article does not contain any studies involving human participants performed by any of the authors.

Additional information

Fatima Zohra Belgrana obtained her engineering degree from University of science and technology of Oran “Mohamed Boudiaf,” USTO-MB, Algeria in 2005. She obtained a Magister degree in 2008 and PhD in 2017 in medical image processing from the same university. She is a member of SIMPA laboratory. She specializes in the area of image processing, distributed systems, artificial intelligence, evolutionary algorithm, neural networks and active contour for medical applications. Belgrana currently holds the position of an associate professor at the University Center “Belhadj Bouchaib” of Ain Temouchent, CUBBAT, Algeria.

Nacéra Benamrane is currently a full professor and a director of SIMPA laboratory in informatics department at University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB). She received her engineering degree in Computer Science from University of Oran, the M.Sc. and Ph.D. degrees from University of Valenciennes, France Since 2002, she is the head of vision and medical imaging team at SIMPA laboratory. She has published more than 100 papers in journals and conference proceedings. Her main research interests include image processing, medical imaging, computer vision, biomedical engineering, and pattern recognition.

Sid Ahmed Kasmi obtained his engineering degree from University of science and technology of Oran “Mohamed Boudiaf,” USTO-MB, Algeria. He obtained a Master degree from the same university in 2017. He actually holds the position of teacher in mathematics.

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Belgrana, F.Z., Benamrane, N. & Kasmi, S.A. A Hybrid Segmentation Approach of Brain Magnetic Resonance Imaging Using Region-Based Active Contour with a Similarity Factor and Multi-Population Genetic Algorithm. Pattern Recognit. Image Anal. 30, 765–777 (2020). https://doi.org/10.1134/S1054661820040069

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