A new region growing algorithm is proposed for the automated segmentation of three-dimensional images. No initial parameters such as the homogeneity threshold or the seeds location have to be adjusted. The principle of the authors' method is to build a region growing sequence in increasing the maximal homogeneity threshold from a very small value to large one. On each segmented region, a 3D parameter which has been validated on a test image, evaluates the segmentation quality. This set of values called evaluation function is used to the determination of the best segmentation. The authors' algorithm was tested on 3D MR images for the segmentation of trabecular bone samples in order to quantify osteoporosis. A comparison to automated and manual thresholding showed that the authors' algorithm performs better. Its main advantages are to eliminate isolated points due to the noise and to preserve connectivity of the bone structure.
['Chantal Revol-Muller', 'Françoise Peyrin', 'Christophe Odet', 'Y. Carillon']
Automated 3D region growing algorithm governed by an evaluation function