Dual-modality 3D brain PET-CT image segmentation based on probabilistic brain atlas and classification fusion

The increasing prevalence of dual medical imaging modalities, such as PET-CT scanners, poses both challenges and opportunities to image segmentation, as they provide distinct but complementary information. In this paper, we propose a novel segmentation algorithm for 3D brain PET-CT images, which classifies each voxel by fusing the voxel's memberships estimated from four points of view using the PET information, CT information, smoothness prior, and probabilistic brain atlas. All memberships having the same dynamic range greatly facilitates weighting the contribution of the four different information sources. The probabilistic brain atlas estimated for each PET-CT image from a set of training samples provides the anatomical information to the segmentation process. We compared the proposed algorithm to three single-classifier based methods, PET-based SPM algorithm, CT-based Otsu thresholding, and PET-CT based MAP-MRF algorithm. The experimental results in 11 clinical brain PET-CT studies demonstrate that the novel algorithm is capable of providing more accurate and reliable segmentation.