Cárdenes Rubén, de Luis-García Rodrigo, Bach-Cuadra Meritxell
Laboratory of Image Processing, University of Valladolid, Valladolid, Spain.
Comput Methods Programs Biomed. 2009 Nov;96(2):108-24. doi: 10.1016/j.cmpb.2009.04.009. Epub 2009 May 14.
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.
分割方法的评估是图像处理中的一个关键方面，尤其是在医学成像领域，解剖结构中分割区域之间的微小差异可能至关重要。通常，分割评估基于一种依赖于某些被称为金标准的参考区域内外分割体素数量的度量。尽管也使用了一些其他度量，但在这项工作中，我们基于不同特征提出了一组新的相似性度量，例如误分类体素的位置和强度值，以及分割数据的连通性和边界。利用这些度量提供的多维信息，我们提出了一种新的评估方法，其结果通过对数据进行主成分分析来可视化，从而获得一种简化的图形方法来比较不同的分割结果。我们使用了几种经典分割方法对具有不同噪声和射频不均匀性水平的一组大脑MRI模拟数据以及真实数据进行了深入研究，结果表明这里提出的新度量以及我们从多维评估中获得的结果提高了评估的稳健性，并能更好地理解分割方法之间的差异。
