Automated 3D region growing algorithm governed by an evaluation function

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