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Proceedings Paper

Technique for evaluation of semiautomatic segmentation methods
Author(s): Fei Mao; Jeremy D. Gill; Aaron Fenster
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Paper Abstract

In this paper we describe an evaluation technique that quantifies both the accuracy and variability in semiautomatic segmentation algorithms. The particular interest of the study is the evaluation of an active contour method for 2-D carotid artery lumen segmentation in ultrasound images. The active contour method used is known as the Geometrically Deformed Model (GDM). This segmentation method to be evaluated requires a single seed to be placed in the target region by the operator. The evaluation approach is based on the contour probability distribution (CPD), which is obtained by generating contours of the object using a set of possible seed locations. A contour matching procedure provides local displacement measures between any two contours, which in turn allow the calculation of the local CPD of a group of contours. The mean contour can be compared to an operator defined contour to provide accuracy measurements, and the variance can provide measures of local and global variability. The evaluation results from multiple images can be pooled to generate statistics for a more complete evaluation of a semi- automatic segmentation method.

Paper Details

Date Published: 21 May 1999
PDF: 10 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999);
Show Author Affiliations
Fei Mao, John P. Robarts Research Institute (Canada)
Jeremy D. Gill, John P. Robarts Research Institute (Canada)
Aaron Fenster, John P. Robarts Research Institute (Canada)

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)

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