A formal method for selecting evaluation metrics for image segmentation

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 particu...

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Bibliographic Details
Main Authors: Taha, Abdel Aziz, Hanbury, Allan, Jimenez del Toro, Oscar A.
Format: Conference Proceeding
Language:English
Subjects:
Correlation
Equations
evaluation metrics
Image segmentation
Information retrieval
Manuals
selection
Sensitivity
Citations: Items that cite this one
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Summary: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 towards/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.
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2014.7025187