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Medical Image Processing pp 27–57Cite as

Seeded Segmentation Methods for Medical Image Analysis

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Part of the book series: Biological and Medical Physics, Biomedical Engineering ((BIOMEDICAL))

Abstract

Segmentation is one of the key tools in medical image analysis. The objective of segmentation is to provide reliable, fast, and effective organ delineation. While traditionally, particularly in computer vision, segmentation is seen as an early vision tool used for subsequent recognition, in medical imaging the opposite is often true. Recognition can be performed interactively by clinicians or automatically using robust techniques, while the objective of segmentation is to precisely delineate contours and surfaces. This can lead to effective techniques known as “intelligent scissors” in 2D and their equivalent in 3D.

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Notes

  1. 1.

    This particular computation is left as an exercise to the reader.

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Authors and Affiliations

  1. Université Paris-Est, Paris, France

    Camille Couprie, Laurent Najman & Hugues Talbot

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  1. Camille Couprie
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  2. Laurent Najman
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  3. Hugues Talbot
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Correspondence to Hugues Talbot .

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  1. , Applied Physics and Medical Imaging, California State University Channel Isla, One University Drive, Camarillo, 93012, USA

    Geoff Dougherty

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Couprie, C., Najman, L., Talbot, H. (2011). Seeded Segmentation Methods for Medical Image Analysis. In: Dougherty, G. (eds) Medical Image Processing. Biological and Medical Physics, Biomedical Engineering. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9779-1_3

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