Label fusion method based on sparse patch representation for the brain MRI image segmentation. Issue 7 (2nd June 2017)

• Record Type: Journal Article
• Authors: Liu, Hong; Yan, Meng; Song, Enmin; Qian, Yuejing; Xu, Xiangyang; Jin, Renchao; Jin, Lianghai; Hung, Chih‐Cheng
• Is Part Of: IET image processing. Volume 11:Issue 7(2017)
• Page Start: 502
• Page End: 511
• Publication Date: 2017-06-02
• Subjects: biomedical MRI -- medical image processing -- image segmentation -- image representation -- feature extraction; label fusion method -- sparse patch representation -- brain MRI image segmentation -- multiAtlas patch‐based label fusion method -- magnetic resonance imaging image segmentation -- feature extraction -- nonlocal‐patch‐based label fusion method -- NPBM; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0988
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.