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A framework for evaluating image segmentation algorithms
Journal article   Open access   Peer reviewed

A framework for evaluating image segmentation algorithms

Jayaram K. Udupa, Vicki R. LeBlanc, Ying Zhuge, Celina Imielinska, Hilary Schmidt, Leanne M. Currie, Bruce E. Hirsch and James Woodburn
Computerized medical imaging and graphics, v 30(2)
2006
DOI: https://doi.org/10.1016/j.compmedimag.2005.12.001
PMID: 16584976
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.compmedimag.2005.12.001View
Published, Version of Record (VoR)Open Access (License Unspecified),  Open

Abstract

Evaluation of segmentation Image analysis Image segmentation Segmentation efficacy
The purpose of this paper is to describe a framework for evaluating image segmentation algorithms. Image segmentation consists of object recognition and delineation. For evaluating segmentation methods, three factors—precision (reliability), accuracy (validity), and efficiency (viability)—need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit, repeat segmentation considering all sources of variation, and determine variations in figure of merit via statistical analysis. It is impossible usually to establish true segmentation. Hence, to assess accuracy, we need to choose a surrogate of true segmentation and proceed as for precision. In determining accuracy, it may be important to consider different ‘landmark’ areas of the structure to be segmented depending on the application. To assess efficiency, both the computational and the user time required for algorithm training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency factors have an influence on one another. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors, as illustrated in an example wherein two methods are compared in a particular application domain. The weight given to each factor depends on application.

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307 citations in Web of Science
363 citations in Scopus

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Biomedical
Radiology, Nuclear Medicine & Medical Imaging
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