Medical image segmentation using 3-D seeded region growing

A flexible framework for medical image segmentation has been developed. The semi-automatic method effectively segments imaging data volumes through the use of 3-D region growing guided by initial seed points. Seed voxels may be specified interactively with a mouse or through the selection of intensity thresholds. Segmentation proceeds automatically following seed selection on only a few slices in the volume due to the 3-D nature of the region growth. Computational efficiency is realized by utilizing fast data structures. The 3-D region growing algorithm has been used for a variety of segmentation tasks. Magnetic resonance (MR) brain volumes acquired at all three imaging orientations have been accurately segmented. The method also was applied to clinical short-axis cardiac data sets for the extraction of the endocardial blood pool. Additionally, preliminary results indicate that myocardial infarcts from high resolution MR images of formalin-fixed hearts may be segmented using our region growing approach. The algorithm is not confined to a particular imaging modality or orientation. It makes use of information in the third dimension, resulting in increased accuracy. Moreover, the entire method can be implemented in a short amount of time due to its simplicity.