Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1016/j.cmpb.2009.04.009 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
multidimensional segmentation evaluation,medical image data,different segmentation result,segmentation method,segmented data,mri simulated data,new measure,classic segmentation method,new evaluation method,multidimensional evaluation,segmentation evaluation,image segmentation,principal component analysis,image processing,gold standard | Voxel,Computer vision,Scale-space segmentation,Pattern recognition,Similarity measure,Computer science,Medical imaging,Segmentation,Image processing,Segmentation-based object categorization,Image segmentation,Artificial intelligence | Journal |
Volume | Issue | ISSN |
96 | 2 | 1872-7565 |
Citations | PageRank | References |
23 | 1.26 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rubén Cárdenes | 1 | 116 | 13.02 |
| Rodrigo de Luis-García | 2 | 150 | 14.15 |
| Meritxell Bach-Cuadra | 3 | 45 | 2.62 |