Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation
Details
Download: BIB_524777162C71.P001.pdf (550.51 [Ko])
State: Public
Version: Final published version
State: Public
Version: Final published version
Serval ID
serval:BIB_524777162C71
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Weighted Shape-based Averaging with Neighborhood Prior Model for Multiple Atlas Fusion-based Medical Image Segmentation
Journal
IEEE Signal Processing Letters
ISSN
1070-9908
Publication state
Published
Issued date
11/2013
Peer-reviewed
Oui
Volume
20
Number
11
Pages
1036-1039
Language
english
Abstract
In medical imaging, merging automated segmentations obtained from multiple atlases has become a standard practice for improving the accuracy. In this letter, we propose two new fusion methods: "Global Weighted Shape-Based Averaging" (GWSBA) and "Local Weighted Shape-Based Averaging" (LWSBA). These methods extend the well known Shape-Based Averaging (SBA) by additionally incorporating the similarity information between the reference (i.e., atlas) images and the target image to be segmented. We also propose a new spatially-varying similarity-weighted neighborhood prior model, and an edge-preserving smoothness term that can be used with many of the existing fusion methods. We first present our new Markov Random Field (MRF) based fusion framework that models the above mentioned information. The proposed methods are evaluated in the context of segmentation of lymph nodes in the head and neck 3D CT images, and they resulted in more accurate segmentations compared to the existing SBA.
Keywords
Biomedical imaging, Image segmentation, Signal processing, Atlas-based segmentation, MRF, SBA, Label fusion
Web of science
Create date
29/08/2013 14:13
Last modification date
20/08/2019 14:07