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International Workshop on Visual Form

IWVF 2001: Visual Form 2001 pp 295–302Cite as

Adaptive Segmentation of MR Axial Brain Images Using Connected Components

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2059))

Abstract

The role of connected components and connected filters is well established. In this paper a new segmentation procedure is presented based on connected components and connected filters. The use of connected components simplified the development of the algorithm. Moreover, if connected components are available as a basic data type, implementation is achievable without resorting to pixel level processing. Using parallel platforms with hardware support for connected components, the algorithm can fully exploit its data parallel implementation. We apply our segmentation procedure to axially oriented magnetic resonance brain images. Novel ideas are presented of how connected components operations (e.g. moments and bounding boxes) and connected filtering (e.g. area close-opening) can be effectively used together.

This work was in part supported by the Erasmus Intensive Programme IP2000

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Author information

Authors and Affiliations

  1. DIS — Università di Pavia, Via Ferrata, 1, Pavia, Italy

    Alberto Biancardi

  2. CVSSP — University of Surrey, Guildford, UK

    Manuel Segovia-Martínez

Authors
  1. Alberto Biancardi
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  2. Manuel Segovia-Martínez
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Editor information

Editors and Affiliations

  1. Istituto di Cibernetica, Via Toiano 6, 80072, Arco Felice (Naples), Italy

    Carlo Arcelli  & Gabriella Sanniti di Baja  & 

  2. Dipartimento di Informatica e Sitemistica, Universitá di Napoli “Frederico II”, Via Claudio 21, 80125, Naples, Italy

    Luigi P. Cordella

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© 2001 Springer-Verlag Berlin Heidelberg

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Biancardi, A., Segovia-Martínez, M. (2001). Adaptive Segmentation of MR Axial Brain Images Using Connected Components. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_26

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  • DOI: https://doi.org/10.1007/3-540-45129-3_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42120-7

  • Online ISBN: 978-3-540-45129-7

  • eBook Packages: Springer Book Archive

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