ACM-Based Automatic Liver Segmentation From 3-D CT Images by Combining Multiple Atlases and Improved Mean-Shift Techniques

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ACM-Based Automatic Liver Segmentation From 3-D CT Images by Combining Multiple Atlases and Improved Mean-Shift Techniques

Ji, Hongwei - Nama Orang; He, Jiangping - Nama Orang; Yang, Xin - Nama Orang; Deklerck, Rudi - Nama Orang; Cornelis, Jan - Nama Orang;

Abstract—In this paper, we present an autocontext model (ACM)-basedautomaticliversegmentationalgorithm,whichcombines ACM, multiatlases, and mean-shift techniques to segment liver from 3-D CT images. Our algorithm is a learning-based method and can be divided into two stages. At the ﬁrst stage, i.e., the training stage, ACM is performed to learn a sequence of classiﬁers in each atlas space (based on each atlas and other aligned atlases). With the use of multiple atlases, multiple sequences of ACM-based classiﬁers are obtained. At the second stage, i.e., the segmentation stage, the test image will be segmented in each atlas space by applying each sequence of ACM-based classiﬁers. The ﬁnal segmentation result will be obtained by fusing segmentation results from all atlas spaces via a multiclassiﬁer fusion technique. Specially, in order to speed up segmentation, given a test image, we ﬁrst use an improved mean-shift algorithm to perform oversegmentationandthenimplementtheregion-basedimagelabeling instead of the original inefﬁcient pixel-based image labeling. The proposed method is evaluated on the datasets of MICCAI 2007 liver segmentation challenge. The experimental results show that theaveragevolumeoverlaperrorandtheaveragesurfacedistance achievedbyourmethodare8.3%and1.5m,respectively,whichare comparable to the results reported in the existing state-of-the-art work on liver segmentation.

Index Terms—Autocontext model (ACM), fuzzy integral, liver segmentation, mean shift, multiclassiﬁer fusion, multiple atlases.

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Judul Seri: -
No. Panggil: -
Penerbit: : IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013
Deskripsi Fisik: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 3, MAY 2013 p. 690-698
Bahasa: English
ISBN/ISSN: 2168-2194
Klasifikasi: NONE
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Subjek: TEKNOLOGI KEDOKTERAN
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Pernyataan Tanggungjawab: Hongwei Ji ... [et al]

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ACM-Based Automatic Liver Segmentation From 3-D CT Images by Combining Multiple Atlases and Improved Mean-Shift Techniques

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