Matching Filtering by Region-Based Attributes on Hierachical Structures for Image Co-Segmentation

Inter / intra operator errors and high-time consumption induced by manual delineation, are the main drawbacks nowadays in clinical PET tumor segmentation. Several methodologies have been proposed to automate this task. However, there is not yet a validated general protocol to use in clinical routine. Multimodality imaging has been shown to provide good performance, taking into account both functional and anatomical scopes together for segmentation decision. In this context, the involved images used are generally required to be spatially corresponding. However, this is not always the case due to acquisition constraints or for multidate follow-up. In this work, we propose a spatially independent algorithm that avoids image pre-processing (e.g. image registration) or acquisition adjustments for multimodal segmentation. In particular, non-spatially correspondent images (such as multitemporal ones) can be directly exploited taking advantage of hierarchical image structure properties. Regions, obtained from hierarchical models of images, are co-evaluated to match similar ones such as tumors on PET and CT. Results show good performance in terms of time-computing and robust-nesses dealing with PET/CT segmentation problems such as necrosis, compared with other methodologies.