Three-dimensional image segmentation using a split, merge and group approach
Pattern Recognition Letters
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
A multidimensional segmentation evaluation for medical image data
Computer Methods and Programs in Biomedicine
Structure-preserving smoothing of biomedical images
Pattern Recognition
Wood detection and tracking in videos of rivers
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Space-time spectral model for object detection in dynamic textured background
Pattern Recognition Letters
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This paper is a joint effort between five institutions that introduces several novel similarity measures and combines them to carry out a multimodal segmentation evaluation. The new similarity measures proposed are based on the location and the intensity values of the misclassified voxels as well as on the connectivity and the boundaries of the segmented data. We show experimentally that the combination of these measures improves the quality of the evaluation, increasing the significance between different methods both visually and numerically and providing better understanding about their difference. The study shown here has been carried out using four different segmentation methods applied to a MRI simulated dataset of the brain.