结合先验稀疏字典和空洞填充的CT图像肝脏分割

王雪虎; 杨健; 艾丹妮; 王涌天

doi:10.3788/OPE.20152309.2687

您当前的位置：

首页 >

文章列表页 >

结合先验稀疏字典和空洞填充的CT图像肝脏分割

信息科学 | 更新时间：2020-08-12

- 结合先验稀疏字典和空洞填充的CT图像肝脏分割
- Liver segmentation in CT image based on priori sparse dictionary and hole filling
- 光学精密工程 2015年23卷第9期页码：2687-2697
- 作者机构：
  
  北京理工大学光电学院北京市混合现实与新型显示工程技术研究中心北京,100081
- 作者简介：
- 基金信息：
  
  国家973重点基础研究发展计划资助项目(No.2013CB328806)();国家863高技术研究发展计划资助项目(No.2013AA013703)();国家十二五科技
- DOI：10.3788/OPE.20152309.2687
  中图分类号： TP391;R814.42
- 收稿日期：2015-03-07，
  
  修回日期：2015-04-28，
  
  纸质出版日期：2015-09-25
- 稿件说明：
移动端阅览
王雪虎, 杨健, 艾丹妮等. 结合先验稀疏字典和空洞填充的CT图像肝脏分割[J]. 光学精密工程, 2015,23(9): 2687-2697

WANG Xue-hu, YANG Jian, AI Dan-ni etc. Liver segmentation in CT image based on priori sparse dictionary and hole filling[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2687-2697
王雪虎, 杨健, 艾丹妮等. 结合先验稀疏字典和空洞填充的CT图像肝脏分割[J]. 光学精密工程, 2015,23(9): 2687-2697 DOI： 10.3788/OPE.20152309.2687.

WANG Xue-hu, YANG Jian, AI Dan-ni etc. Liver segmentation in CT image based on priori sparse dictionary and hole filling[J]. Editorial Office of Optics and Precision Engineering, 2015,23(9): 2687-2697 DOI： 10.3788/OPE.20152309.2687.

摘要

针对复杂多变的肝脏图像

提出了一种基于先验稀疏字典和空洞填充的三维肝脏图像分割方法。对腹部CT图像进行Gabor特征提取

并分别在Gabor图像和灰度图像的肝脏金标准边界上选择大小相同的图像块作为两组训练集

利用训练集得到两种查询字典及稀疏编码。将金标准图像与待分割图像配准

并将配准后的肝脏边界作为待分割图像的肝脏初始边界;在初始边界点上的十邻域内选择大小相同的两组图像块作为测试样本

利用测试样本与查询字典计算稀疏编码及重构误差

并选择重构误差最小的图像块的中心作为待分割肝脏的边界点;最后

设计一种空洞填充方法对肝脏边界进行补全和平滑处理

得到最终分割结果。利用医学图像计算和计算机辅助介入国际会议中提供的肝脏数据进行了实验验证。结果表明

该方法对肝脏分割图像具有较好的适用性和鲁棒性

并获得了较高的分割精度。其中

平均体积重叠率误差为(5.21±0.45)%

平均相对体积误差为(0.72±0.12)%

平均对称表面距离误差为(0.93±0.14) mm。

Abstract

For complicated liver images

this paper presents a three-dimensional automatic liver segmentation method based on sparse dictionary and hole filling technologies.The Gabor feature of an abdominal CT image was extracted. The image blocks with the same size on the border of liver gold standard in Gabor images and CT images were selected as two groups of train sets. Then

the training sets were used to get the dictionaries and sparse coding. The golden standard image was registered with the image to be segmented

and registered liver boundary was taken as the initial liver boundary of the image to be segmented. Furthermore

two sets of images with the same size were selected as the training sets in ten neighborhoods on the initial boundary. The sparse coding and image reconstruction error were computed by using the testing sets and the block-sparse dictionary

and the final liver boundary with the smallest image reconstruction error was obtained. Finally

a hole filling method was designed for liver boundary completion and smoothing to obtain the final segmentation results. The proposed method for the liver segmentation was evaluated by using the data sets of MICCAI 2007. The results show that this method has better segmentation applicability and robustness for the liver. It shows a higher segmentation accuracy

the volume overlap error rate is reduced to 5.21±0.004 5

the relative volume error is 0.72±0.001 2

and the average symmetric surface distance error is reduced to (0.93±0.14) mm.

关键词

Keywords

references

ANTER A M, AZAR A T, HASSANIEN A E, et al.. Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques [C]. Federated Conference on Computer Science and Information Systems (FedCSIS), 2013:193-198.

MHARIB A M, RAMLI A R, MASHOHOR S, et al.. Survey on liver CT image segmentation methods [J]. Artificial Intelligence Review, 2012, 37(2):83-95.

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.

SUHUAI LUO X L, JJANING LI. Review on the methods of automatic liver segmentation from abdominal images [J].Journal of Computer and Communications, 2014, 2(2):1-7.

CAMPADELLI P, CASIRAGHI E, ESPOSITO A. Liver segmentation from computed tomography scans:A survey and a new algorithm [J]. Artificial Intelligence in Medicine, 2009, 45(2-3):185-196.

WRIGHT J, YANG A Y, GANESH A, et al.. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210-227.

ROUSSEAU F, HABAS P A, STUDHOLME C. A supervised patch-based approach for human brain labeling [J].IEEE Transactions on Medical Imaging, 2011, 30(10):1852-1862.

JIANG Z L, LIN Z, DAVIS L S. Learning a discriminative dictionary for sparse coding via label consistent K-SVD [C].IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011:1697-1704.

COUPE P, MANJON J V, FONOV V, et al.. Patch-based segmentation using expert priors:Application to hippocampus and ventricle segmentation [J]. Neuroimage, 2011, 54(2):940-954.

LIAO S, GAO Y Z, LIAN J, et al.. Sparse patch-based label propagation for accurate prostate localization in CT images [J]. IEEE Transactions on Medical Imaging, 2013, 32(2):419-434.

ZHANG S, ZHAN Y, METAXAS D N. Deformable segmentation via sparse representation and dictionary learning [J].Med Image Anal, 2012, 16(7):1385-96.

吴迪,王奎民,赵玉新,等. 分段正则化正交匹配追踪算法[J]. 光学精密工程,2014,22(5):1395-1402. WU D, WANG K M, ZHAO Y X, et al.. Stagewise regularized orthogonal matching pursuit algorithm [J].Opt. Precision Eng., 2014,22(5):1395-1402. (in Chinese)

CHEN T, WANG X X, CHUNG S, et al. Automated 3D motion tracking using Gabor filter bank, robust point matching and deformable models [J]. IEEE Transactions on Medical Imaging, 2010, 29(1):1-11.

SHEN L L, BAI L. 3D Gabor wavelets for evaluating SPM normalization algorithm [J].Medical Image Analysis, 2008, 12(3):375-383.

YUN T, GUAN L. Human emotional state recognition using real 3D visual features from Gabor library [J].Pattern Recognition, 2013, 46(2):529-538.

MING Y, RUAN Q G, WANG X G. Efficient 3D face recognition with Gabor patched spectral regression [J]. Computing and Informatics, 2012, 31(4):779-803.

QIAN Z, METAXAS D N, AXEL L. Extraction and tracking of MRI tagging sheets using a 3D Gabor filter bank [C].28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006:5184-5187.

CHEN T, WANG X X, CHUNG S, et al..Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models [J]. IEEE Transactions on Medical Imaging, 2010, 29(1):1-11.

PLATAS-GARZA M A, SERNA J A D. Polynomial implementation of the taylor-fourier transform for harmonic analysis [J]. IEEE Transactions on Instrumentation and Measurement, 2014, 63(12):2846-2854.

穆治亚,魏仲慧,何昕等. 采用稀疏表示的红外线图像自适应杂波抑制[J]. 光学精密工程,2013,21(7):1850-1857. MU ZH Y, WEI ZH H, HE X, et al.. Adaptive clutter suppression of infrared images using sparse representation [J]. Opt. Precision Eng., 2013,21(7):1850-1857. (in Chinese)

AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:An algorithm for designing over-complete dictionaries for sparse representation [J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.

SYLVAIN L, REMI G, FREDERIC B, et al.. Learning unions of orthonormal bases with thresholded singular value decomposition [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005:293-296.

JU T. Robust repair of polygonal models [J].Acm Transactions on Graphics, 2004, 23(3):888-895.

ZHANG X, TIAN J, DENG K X, et al.. Automatic liver segmentation using a statistical shape model with optimal surface detection [J]. IEEE Transactions on Biomedical Engineering, 2010, 57(10):2622-2626.

OLIVEIRA DA, FEITOSA RQ, CORREIA MM. Segmentation of liver, its vessels and lesions from CT images for surgical planning [J].Biomedical Eng. Online, 2011,10:10-30.

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

李春丽,张久楼,冯前进. 基于统计信息及个体信息的统计形状模型的肝脏分割[J]. 南方医科大学学报,2012,32(1):23-27. LI CH L, ZHANG J L, FENG Q J. Liver CT image segmentation using statistical shape model based on statistical and specific information [J].J South Med Univ., 2012, 32(1):23-27. (in Chinese)

浏览量

420

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于跨尺度字典学习的图像盲解卷积算法

改进的稀疏表示遥感图像超分辨重建

基于稀疏编码空间金字塔匹配和GA-SVM的列车故障自动识别

数据保真项与稀疏约束项相融合的稀疏重建