%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{,
     author = {Taha, Abdel Aziz and Hanbury, Allan and Jimenez del Toro, Oscar},
   keywords = {evaluation metrics, image segmentation, selection},
      title = {A Formal Method For Selecting Evaluation Metrics For Image Segmentation},
  booktitle = {IEEE International Conference on Image Processing (ICIP) 2014},
       year = {2014},
   location = {Paris, France},
        url = {http://ifs.tuwien.ac.at/~taha/1569912083.pdf},
        doi = {978-1-4799-5751-4/14},
   abstract = {Evaluating the quality of segmentations is an important process in image processing, especially in the medical domain. Many evaluation metrics have been used in evaluating segmentation. There exists no formal way to choose the most suitable metric(s) for a particular segmentation task and/or particular data. In this paper we propose a formal method for choosing the most suitable metrics for evaluating the quality of segmentations with respect to ground truth segmentations. The proposed method depends on measuring the bias of metrics  owards/against the properties of the the segmentations
being evaluated. We firstly demonstrate how metrics can have bias towards/against particular properties and then we propose a general method for ranking metrics according to their overall bias. We finally demonstrate for 3D medical image segmentations that ranking produced using metrics with low overall bias strongly correlate with manual rankings done by an expert.},
2014 IEEE International Conference on Image Processing (ICIP)
Conference Details
Dates
27 Oct - 30 Oct 2014
}