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A Framework for Active Contour Initialization with Application to Liver Segmentation in MRI

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1602))

Abstract

Object segmentation is a prominent low-level task in image processing and computer vision. A technique of special relevance within segmentation algorithms is active contour modeling. An active contour is a closed contour on an image which can be evolved to progressively fit the silhouette of certain area or object. Active contours shall be initialized as a closed contour at some position of the image, further evolving to precisely fit to the silhouette of the object of interest. While the evolution of the contour has been deeply studied in literature [5, 11], the study of strategies to define the initial location of the contour is rather absent from it. Typically, such contour is created as a small closed curve around an inner position in the object. However, literature contains no general-purpose algorithms to determine those inner positions, or to quantify their fitness. In fact, such points are frequently set manually by human experts, hence turning the segmentation process into a semi-supervised one. In this work, we present a method to find inner points in relevant object using spatial-tonal fuzzy clustering. Our proposal intends to detect dominant clusters of bright pixels, which are further used to identify candidate points or regions around which active contours can be initialized.

The authors gratefully acknowledge the financial support of the grants PID2019-108392GB-I00 funded by MCIN/AEI/10.13039/501100011033, as well as that by the Government of Navarra (PC082-083-084 EHGNA). A. Mir acknowledges the financial support of the grant PID2020-113870GB-I00 funded by MCIN/AEI/10.13039/501100011033/.

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Authors and Affiliations

  1. Department of Estadística, Informática y Matemáticas, Universidad Pública de Navarra, 31006, Pamplona, Spain

    Arnau Mir-Fuentes, Felipe Antunes-Santos, F. Javier Fernandez & Carlos Lopez-Molina

  2. NavarraBiomed, Hospital Universitario de Navarra, 31008, Pamplona, Spain

    Arnau Mir-Fuentes, Felipe Antunes-Santos & Carlos Lopez-Molina

  3. KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, 9000, Ghent, Belgium

    Felipe Antunes-Santos & Carlos Lopez-Molina

  4. Department of Mathematics and Computer Science, University of the Balearic Islands, 07122, Palma, Spain

    Arnau Mir

  5. Health Research Institute of the Balearic Islands (IdISBa), 07010, Palma, Spain

    Arnau Mir

Authors
  1. Arnau Mir-Fuentes
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  2. Arnau Mir
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  3. Felipe Antunes-Santos
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  4. F. Javier Fernandez
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  5. Carlos Lopez-Molina
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Corresponding author

Correspondence to Arnau Mir-Fuentes .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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Mir-Fuentes, A., Mir, A., Antunes-Santos, F., Fernandez, F.J., Lopez-Molina, C. (2022). A Framework for Active Contour Initialization with Application to Liver Segmentation in MRI. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_21

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  • DOI: https://doi.org/10.1007/978-3-031-08974-9_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-08974-9

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