Matching filtering by region-based attributes on hierarchical structures for image co-segmentation - Archive ouverte HAL Access content directly
Conference Papers Year : 2018

Matching filtering by region-based attributes on hierarchical structures for image co-segmentation

(1) , (1) , (2) , (1, 3) , (1, 4) , (1)
1
2
3
4

Abstract

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 necro-sis, compared with other methodologies.
Fichier principal
Vignette du fichier
Alvarez_Padilla_2018.pdf (610.12 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01706566 , version 1 (12-02-2018)

Identifiers

Cite

Francisco Javier Alvarez Padilla, Barbara Romaniuk, Benoît Naegel, Stéphanie Servagi-Vernat, Dimitri Papathanassiou, et al.. Matching filtering by region-based attributes on hierarchical structures for image co-segmentation. International Conference on Image Processing (ICIP), 2018, Athens, Greece. pp.131-135, ⟨10.1109/ICIP.2018.8451384⟩. ⟨hal-01706566⟩
102 View
143 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More