A large variety of image analysis tasks require the segmentation of various regions in an image. For example,
segmentation is required to generate accurate models of brain pathology that are important components of
modern diagnosis and therapy. While the manual delineation of such structures gives accurate information,
the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the
speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to
a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The
evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than
developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using
similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing
the parameter settings for individual cases and across a collection of datasets using the Design of Experiment
framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms.
We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that
of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized
parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation
tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the
statistical segmentation algorithm although they perform reasonably well considering their simplicity.