Abstract-Dual modality PET/CT has now essentially replaced PET in clinical practice and provided an opportunity to improve image segmentation through the high resolution, lower noise CT data. Thus far most research efforts have concentrated on segmentation of PET-only data. In this work we propose a systematic solution for the automated segmentation of brain PET/CT images into gray, white matter and CSF regions with the MAP-MRF model. Our approach takes advantage of the full information available from the combined scan. A PET/CT image pair and its segmentation result are modelled as a random field triplet, and segmentation is eventually achieved by solving a maximum a posteriori (MAP) problem using the expectationmaximization (EM) algorithm with simulated annealing. We compared the novel algorithm to two widely used PET-only based segmentation methods in the SPM5 toolbox and the VBM toolbox for simulation and patient data. Our results suggest that using the proposed approach substantially improves the accuracy of the delineation of brain structures.
Xia et al. REF presented a systematic solution for the co-segmentation of brain PET-CT images with the MAP-MRF model, which was achieved by solving a maximum a posteriori (MAP) problem using the expectation-maximization (EM) algorithm with simulated annealing.
Segmentation of dual modality brain PET/CT images using the MAP-MRF model
2008 IEEE 10th Workshop on Multimedia Signal Processing
2008 IEEE 10th Workshop on Multimedia Signal Processing
Computer Science