Multi-stage thresholded region classification for whole-body PET-CT lymphoma studies

Positron emission tomography computed tomography (PET-CT) is the preferred imaging modality for the evaluation of the lymphomas. Disease involvement in the lymphomas usually appear as foci of increased Fluorodeoxyglucose (FDG) uptake. Thresholding methods are applied to separate different regions of involvement. However, the main limitation of thresholding is that it also includes regions where there is normal FDG excretion and FDG uptake (NEUR) in structures such as the brain, bladder, heart and kidneys. We refer to these regions as NEURs (the normal excretion and uptake (of FDG) regions). NEURs can make image interpretation problematic. The ability to identify and label NEURs and separate them from abnormal regions is an important process that could improve the sensitivity of lesion detection and image interpretation. In this study, we propose a new method to automatically separate NEURs in thresholded PET images. We propose to group thresholded regions of the same structure with spatial and texture based clustering; we then classified NEURs on PET-CT contextual features. Our findings were that our approach had better accuracy when compared to conventional methods.