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We present a novel 3-D region-based hidden Markov model (rbHMM) for efficient unsupervised 3-D image segmentation. Our contribution is twofold. First, rbHMM employs a more efficient representation of the image data than current state-of-the-art HMM-based approaches that are based on either voxels or rectangular lattices/grids, thus resulting in a faster optimization process. Second, our proposed novel tree-structured parameter estimation algorithm for the rbHMM provides a locally optimal data labeling that is invariant to object rotation, which is a highly valuable property in segmentation tasks, especially in medical imaging where the segmentation results need to be independent of patient positioning in scanners in order to minimize methodological variability in data analysis. We demonstrate the advantages of our proposed technique over grid-based HMMs by validating on synthetic images of geometric shapes as well as both simulated and clinical brain MRI scans. For the geometric shapes data, our method produced consistently accurate segmentation results that were also invariant to object rotation. For the brain MRI data, our white matter and gray matter segmentation resulted in substantially higher robustness and accuracy levels with improved Dice similarity indices of 4.60% (p=0.0022) and 7.71% (p<;0.0001) , respectively.