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Automated 3D region growing algorithm based on an assessment function

2002; Elsevier BV; Volume: 23; Issue: 1-3 Linguagem: Inglês

10.1016/s0167-8655(01)00116-7

1872-7344

Chantal Revol-Muller, Françoise Peyrin, Yannick Carrillon, Christophe Odet,

Image Retrieval and Classification Techniques

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 our method is to build a region growing sequence by increasing the maximal homogeneity threshold from a very small value to a large one. On each segmented region, a 3D parameter that has been validated on a test image assesses the segmentation quality. This set of values called assessment function is used to determine of the optimal homogeneity criterion. Our 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 our algorithm performs better. Its main advantages are to eliminate isolated points due to the noise and to preserve connectivity of the bone structure.