RT Journal Article
A1 Hong Liu
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Meng Yan
AD Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan, Hubei, People's Republic of China
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Enmin Song
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Yuejing Qian
AD Zhejiang Industry & Trade Vocational College, Wenzhou, Zhejiang, People's Republic of China
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Xiangyang Xu
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Renchao Jin
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Lianghai Jin
AD Key Laboratory of Education Ministry for Image Processing and Intelligence Control, Wuhan, Hubei, People's Republic of China
AD School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China
A1 Chih-Cheng Hung
AD Center for Machine Vision and Security Research, Kennesaw State University, Marietta, Georgia, USA

PB iet
T1 Label fusion method based on sparse patch representation for the brain MRI image segmentation
JN IET Image Processing
VO 11
IS 7
SP 502
OP 511
AB The multi-Atlas patch-based label fusion method (MAS-PBM) has emerged as a promising technique for the magnetic resonance imaging (MRI) image segmentation. The state-of-the-art MAS-PBM approach measures the patch similarity between the target image and each atlas image using the features extracted from images intensity only. It is well known that each atlas consists of both MRI image and labelled image (which is also called the map). In other words, the map information is not used in calculating the similarity in the existing MAS-PBM. To improve the segmentation result, the authors propose an enhanced MAS-PBM in which the maps will be used for similarity measure. The first component of the proposed method is that an initial segmentation result (i.e. an appropriate map for the target) is obtained by using either the non-local-patch-based label fusion method (NPBM) or the sparse patch-based label fusion method (SPBM) based on the grey scales of patches. Then, the SPBM is applied again to obtain the finer segmentation based on the labels of patches. The authors called these two versions of the proposed fusion method as MAS-PBM-NPBM and MAS-PBM-SPBM. Experimental results show that more accurate segmentation results are achieved compared with those of the majority voting, NPBM, SPBM, STEPS and the hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition.
K1 brain MRI image segmentation
K1 feature extraction
K1 label fusion method
K1 multiAtlas patch-based label fusion method
K1 NPBM
K1 nonlocal-patch-based label fusion method
K1 sparse patch representation
K1 magnetic resonance imaging image segmentation
DO https://doi.org/10.1049/iet-ipr.2016.0988
UL https://digital-library.theiet.org/;jsessionid=1fh2llzjxhi6n.x-iet-live-01content/journals/10.1049/iet-ipr.2016.0988
LA English
SN 1751-9659
YR 2017
OL EN