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Academic Commons Search Resultsen-usA Methodology for Evaluating Image Segmentation Algorithms
https://academiccommons.columbia.edu/catalog/ac:177578
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.http://dx.doi.org/10.7916/D8FQ9V3DMon, 22 Sep 2014 15:45:25 +0000The 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.Biomedical engineering, Computer science, Information technologyci42Center for Education Research and Evaluation, Biomedical Informatics, Biomedical EngineeringConferencesA framework for evaluating image segmentation algorithms
https://academiccommons.columbia.edu/catalog/ac:151518
Udupa, Jayaram K.; LeBlanc, Vicki R.; Zhuge, Ying; Imielinska, Celina Z.; Schmidt, Hilary; Currie, Leanne M.; Hirsch, Bruce E.; Woodburn, Jameshttp://hdl.handle.net/10022/AC:P:14350Mon, 13 Aug 2012 11:19:57 +0000The 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.Medical imaging and radiologyci42Center for Education Research and Evaluation, Radiation Oncology, NursingArticles