Open AccessAutomatic labelling of brain tissues in MR images through spatial indexes based hybrid atlas forest
Author(s) -
Liu Hong,
Xu Lijun,
Song Enmin,
Jin Renchao,
Hung ChihCheng
Publication year - 2020
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/iet-ipr.2018.6073
Subject(s) - atlas (anatomy) , labelling , computer science , artificial intelligence , spatial analysis , pattern recognition (psychology) , brain atlas , data mining , computer vision , remote sensing , geography , geology , paleontology , criminology , sociology
The multi‐atlas‐based methods are widely applied in the automatic labelling in magnetic resonance (MR) images. However, most multi‐atlas‐based methods require that all atlases be registered to the target image accurately to have a correct label propagation. In this study, the authors introduce the term spatial indexes and construct a hybrid atlas forest model to gather the labelling information from all atlases without propagating labels from every single atlas. Furthermore, a new automatic labelling method using the hybrid atlas forest model based on spatial indexes is proposed. In the proposed framework, an atlas is chosen arbitrarily as a reference image and the spatial indexes are constructed on this image space. Then, the samples are selected from all atlases in the dataset based on the spatial indexes to construct a samples pool. Finally, the hybrid atlas forest model will be trained on the samples pool and used to predict the labelling of the target. Experiments are conducted on two public datasets to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method reduces the requirement of strong dependence on precise registration and improve the accuracy of labelling.