Open AccessDouble‐weighted patch‐based label fusion for MR brain image segmentation
Author(s) -
Yan Meng,
Jin Huazhong,
Zhao Zhiqiang,
Xia Dahai,
Pan Ning
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12022
Subject(s) - pixel , segmentation , artificial intelligence , fusion , computer science , image fusion , pattern recognition (psychology) , image segmentation , magnetic resonance imaging , computer vision , atlas (anatomy) , similarity (geometry) , image (mathematics) , medicine , anatomy , radiology , philosophy , linguistics
In recent decades, a large number of label fusion methods have been introduced and applied to magnetic resonance brain image segmentation. This paper proposes a double‐weighted label fusion method to improve the segmentation accuracy of magnetic resonance brain tissues. In weighted label fusion methods, different weights are utilised for different neighbouring pixels around the central test pixel. The weight of one specific neighbouring pixel is determined by the structural similarity between the patch of the atlas that centred in the neighbouring pixel and the patch of the image to be segmented that centred in the central test pixel, which is referred to as a patch‐based label fusion scheme. This paper presents a double‐weighted label fusion method to improve the segmentation accuracy of magnetic resonance brain tissues. The proposed label fusion method adds another new type of weights to the neighbouring pixels around the central test pixel and this new type of weights are calculated based on the atlas information itself. Segmentation experiments of different brain tissues show that our method can improve the segmentation performance.