Conference Objects

A Methodology for Evaluating Image Segmentation Algorithms

Udupa, Jayaram K.; LaBlanc, Vicki R.; Schmidt, Hilary; Imielinska, Celina Z.; Saha, Punam K.; Grevera, George J.; Zhuge, Ying; Molholt, Pat; Jin, Yinpeng; Currie, Leanne M.

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 (reproducibility), accuracy (agreement with truth), and efficiency (time taken) – need to be considered for both recognition and delineation. To assess precision, we need to choose a figure of merit (FOM), repeat segmentation considering all sources of variation, and determine variations in FOM 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. To assess efficiency, both the computational and the user time required for algorithm and operator training and for algorithm execution should be measured and analyzed. Precision, accuracy, and efficiency are interdependent. It is difficult to improve one factor without affecting others. Segmentation methods must be compared based on all three factors. The weight given to each factor depends on application.


  • thumnail for 2002_Imielinska_SPIE_Udupa___LeBlanc__Schmidt.pdf 2002_Imielinska_SPIE_Udupa___LeBlanc__Schmidt.pdf text/pdf 761 KB Download File

Also Published In

Proceedings of SPIE

More About This Work

Academic Units
Center for Education Research and Evaluation
Biomedical Informatics
Biomedical Engineering
Published Here
September 22, 2014