ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2014
Segmentation based denoising of PET images: an iterative approach via regional means and affinity propagation.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Computer-Aided Design and Manufacturing (CAD/CAM) for Bioprinting.Methods in molecular biology (Clifton, N.J.) · 2020Article
- PET image denoising using unsupervised deep learning.European journal of nuclear medicine and molecular imaging · 2019Article
- Joint solution for PET image segmentation, denoising, and partial volume correction.Medical image analysis · 2018Article
- Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans.IEEE transactions on medical imaging · 2017Article
- Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images.IEEE transactions on medical imaging · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
Delineation and noise removal play a significant role in clinical quantification of PET images. Conventionally, these two tasks are considered independent, however, denoising can improve the performance of boundary delineation by enhancing SNR while preserving the structural continuity of local regions. On the other hand, we postulate that segmentation can help denoising process by constraining the smoothing criteria locally. Herein, we present a novel iterative approach for simultaneous PET image denoising and segmentation. The proposed algorithm uses generalized Anscombe transformation priori to non-local means based noise removal scheme and affinity propagation based delineation. For nonlocal means denoising, we propose a new regional means approach where we automatically and efficiently extract the appropriate subset of the image voxels by incorporating the class information from affinity propagation based segmentation. PET images after denoising are further utilized for refinement of the segmentation in an iterative manner. Qualitative and quantitative results demonstrate that the proposed framework successfully removes the noise from PET images while preserving the structures, and improves the segmentation accuracy.
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Registered trials
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