Three-dimensional image segmentation using a split, merge and group approach
Pattern Recognition Letters
Fast k-NN classification for multichannel image data
Pattern Recognition Letters
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Measuring Global and Local Spatial Correspondence Using Information Theory
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Review: A comparative study of deformable contour methods on medical image segmentation
Image and Vision Computing
Similarity measures for mid-surface quality evaluation
Computer-Aided Design
Multimodal evaluation for medical image segmentation
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Two methods for validating brain tissue classifiers
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
Validation of FS+LDDMM by automatic segmentation of caudate nucleus in brain MRI
Proceedings of the 8th International Conference on Frontiers of Information Technology
Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches
Information Sciences: an International Journal
Journal of Mathematical Imaging and Vision
Computer Methods and Programs in Biomedicine
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
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Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.