MAP-MRF segmentation of lung tumours in PET/CT images

The unsupervised maximum a posterior - Markov random field labelling technique for lung tumour segmentation in registered PET/CT imagery is proposed. The technique was applied to a range of PET/CT scan clinical datasets obtained from patients with non-small cell lung cancer. The technique was then extended to use a vector approach to take into account the CT datasets along with the corresponding PET. The performances of both the scalar and vector algorithms were in this case then compared to manual outlines obtained from the four clinicians´ gross tumour volume outlines. Results showed comparable variability with that of the clinicians, with slightly better results returned for the vector technique.