Liver Segmentation from CT Image Based on Sequential Constraint and Multi-view Information Fusion

PENG Jialin; JIE Ping

doi:10.11999/JEIT170933

Volume 40 Issue 4

Apr. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(4): 971-978

PENG Jialin, JIE Ping . Liver Segmentation from CT Image Based on Sequential Constraint and Multi-view Information Fusion[J]. Journal of Electronics & Information Technology, 2018, 40(4): 971-978. doi: 10.11999/JEIT170933

Citation:

PENG Jialin, JIE Ping . Liver Segmentation from CT Image Based on Sequential Constraint and Multi-view Information Fusion[J]. Journal of Electronics & Information Technology, 2018, 40(4): 971-978. doi: 10.11999/JEIT170933

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Liver Segmentation from CT Image Based on Sequential Constraint and Multi-view Information Fusion

doi: 10.11999/JEIT170933

PENG Jialin¹,
JIE Ping ¹

Funds:

The National Natural Science Foundation of China (11771160, 11401231), The Natural Science Foundation of Fujian Province (2015J01254), The Research Promotion Program of Huaqiao University (ZQN-PY411)

Received Date: 2017-10-09
Rev Recd Date: 2018-02-06
Publish Date: 2018-04-19

Abstract

Abstract

The accurate segmentation of liver in medical Computed Tomography (CT) sequence images is important prerequisite for computer-assisted liver surgery. However, the presence of tissue lesions, the blurred or missing boundary and the adhesion between different organs/tissues poses great challenges to liver segmentation. To address these problems, this paper presents a semi-automatic segmentation method based on the sequential constraints of image sequences, and introduces further a multi-view information fusion method to achieve the accurate segmentation of the liver. One advantage of this approach is that it does not need extensive data collection and complicated prior training. The validation and comparison results on the Sliver07 public data show that the proposed method shows competitive performance, especially when there is liver tumor, blurred or missing liver boundary.
- CT sequence image,
- Liver segmentation,
- Prior constraint,
- Multi-view information fusion

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References(25)

References

HEIMANN T, MEINZER H, and WOLF I. A statistical deformable model for the segmentation of liver CT volumes [C]. MICCAI Workshop 3-D Segmentation Clinic Grand Challenge, Brisbane, Australia, 2007: 161-166.

KAINMULLER D, LANGE T, and LAMECKER H. Shape constrained automatic segmentation of the liver based on a heuristic intensity model[C]. MICCAI Workshop 3-D Segmentation Clinic Grand Challenge, Brisbane, Australia, 2007: 109-116.

HEIMANN T and MEINZER H P. Statistical shape models for 3D medical image segmentation: a review[J]. Medical Image Analysis, 2009, 13(4): 543-563. doi: 10.1016/j.media. 2009.05.004.

LI G, CHEN X, SHI F, et al. Automatic liver segmentation based on shape constraints and deformable graph cut in CT images[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5315-5329. doi: 10.1109/TIP.2015.2481326.

PLATERO C and TOBAR M C. A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans[J]. Computational and Mathematical Methods in Medicine, 2014, 2014: 182909. doi: 10.1155/2014/182909.

HU P, WU F, PENG J, et al. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution[J]. Physics in Medicine and Biology, 2016, 61(24): 8676-8698. doi: 10.1088/1361-6560/61/24/8676.

LU F, WU F, HU P, et al. Automatic 3D liver location and segmentation via convolutional neural network and graph cut [J]. International Journal of Computer Assisted Radiology and Surgery, 2017, 12(2): 171-182. doi: 10.1007/s11548-016- 1467-3.

ZHENG Y, AI D, MU J, et al. Automatic liver segmentation based on appearance and context information[J]. Biomedical Engineering Online, 2017, 16(1): 16-28. doi: 10.1186/s12938- 016-0296-5.

HEIMANN T, GINNEKEN B V, STYNER M A, et al. Comparison and evaluation of methods for liver segmentation from CT datasets[J]. IEEE Transactions on Medical Imaging, 2009, 28(8): 1251-1265. doi: 10.1109/TMI.2009.2013851.

ZHENG S, FANF B, LI L, et al. A novel variational method for liver segmentation based on statistical shape model prior and enforced local statistical feature[C]. IEEE International Symposium on Biomedical Imaging, Melbournr, Australia, 2017: 261-264. doi: 10.1109/ISBI.2017.7950515.

SHI C, CHENG Y, LIU F, et al. A hierarchical local region- based sparse shape composition for liver segmentation in CT scans[J]. Pattern Recognition, 2016, 50(C): 88-106. doi: 10.1016/j.patcog.2015.09.001.

RUSKO L, BEKES G, and FIDRICH M. Automatic segmentation of the liver from multi and single-phase contrast-enhanced CT images[J]. Medical Image Analysis, 2009, 13(6): 871-882. doi: 10.1016/j.media.2009.07.009.

AFIFI A and NAKAGUCHI T. Liver segmentation approach using graph cuts and iteratively estimated shape and intensity constrains[C]. Medical Image Computing and Computer-Assisted Intervention, Nice, France, 2012, 7511: 395-403.

CHEN X, UDUPA J K, BAGCI U, et al. Medical image segmentation by combining graph cuts and oriented active appearance models[J]. IEEE Transactions on Image Processing, 2012, 21(4): 2035-2046. doi: 10.1109/TIP.2012. 2186306.

WANG X, YANG J, AI D, et al. Adaptive Mesh Expansion Model (AMEM) for liver segmentation from CT image[J]. PloS One, 2015, 10(3): e0118064. doi: 10.1371/journal.pone. 0118064.

LIAO M, ZHAO Y, WANG W, et al. Efficient liver segmentation in CT images based on graph cuts and bottleneck detection[J]. Physica Medica, 2016, 32(11): 1383-1396. doi: 10.1155/2016/9093721.

WU W, ZHOU Z, WU S, et al. Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts [J]. Computational and Mathematical Methods in Medicine, 2016, 2016: 9093721. doi: 10.1155/2016/9093721.

PENG J, HU P, LU F, et al. 3D liver segmentation using multiple region appearances and graph cuts[J]. Medical Physics, 2015, 42(12): 6840-6852. doi: 10.1118/1.4934834.

YANG X, YU H C, CHOI Y, et al. A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points[J]. Computer Methods and Programs in Biomedicine, 2014, 113(1): 69-79. doi: 10.1016/ j.cmpb.2013.08.019.

PENG J L, WANG Y, and KONG D X. Liver segmentation with constrained convex variational model[J]. Pattern Recognition Letters, 2014, 43(1): 81-88. doi: 10.1016/j.patrec. 2013.07.010.

EAPEN M, KORAH R, and GEETHA G. Computerized liver segmentation from CT images using probabilistic level set approach[J]. Arabian Journal for Science Engineering, 2016, 41(3): 921-934. doi: 10.1007/s13369-015-1871-y.

SONG X, CHENG M, WWAG B, et al. Adaptive fast marching method for automatic liver segmentation from CT images[J]. Medical Physics, 2013, 40(9): 091917-28. doi: 10.1118/1.4819824.

BEICHEL R, BORNIK A, BAUER C, et al. Liver segmentation in contrast enhanced CT data using graph cuts and interactive 3D segmentation refinement methods[J]. Medical Physics, 2012, 39(3): 1361-1373. doi: 10.1118/ 1.3682171.

BOYKOV Y Y and JOLLY M P. Interactive graph cuts for optimal boundary region segmentation of objects in ND images[C]. Proceedings of the Eighth International Conference on Computer Vision, Vancouver, British Columbia, Canada, 2001: 105-112. doi: 0-7695-1143-0/01.

BROX T and MALIK J. Large displacement optical flow: Descriptor matching in variational motion estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(3): 500-513. doi: 10.1109/TPAMI.2010. 143.

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