吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (5): 1688-1696.doi: 10.13229/j.cnki.jdxbgxb201605045

• • 上一篇    下一篇

基于多特征支持向量机和弹性区域生长的膝软骨自动分割

王品1, 何璇1, 吕洋1, 李勇明1,2, 邱明国2, 刘书君1   

  1. 1.重庆大学 通信工程学院,重庆 400044;
    2.第三军医大学 生物医学工程学院,重庆 400038
  • 收稿日期:2015-04-21 出版日期:2016-09-20 发布日期:2016-09-20
  • 作者简介:王品(1979-),女,副教授,博士.研究方向:医学图像处理.E-mail:wangpin@cqu.edu.cn
  • 基金资助:
    国家自然科学基金项目(61108086,11304382); 中国博士后科学基金项目(2013M532153); 重庆市自然科学基金项目(CSTC 2012jjA40015); 重庆市科技攻关计划项目(cstc2012gg-yyjs0572); 中央高校基本科研业务费项目(CDJZR12160011, CDJZR13160008, CDJZR155507).

Automatic segmentation of articular cartilages using multi-feature SVM and elastic region growing

WANG Pin1, HE Xuan1, LYU Yang1, LI Yong-ming1,2, QIU Ming-guo2, LIU Shu-jun1   

  1. 1.College of Communication Engineering,Chongqing University, Chongqing 400044,China;
    2.College of Biomedical Engineering, The Third Military Medical University, Chongqing 400038,China
  • Received:2015-04-21 Online:2016-09-20 Published:2016-09-20

摘要: 为了从膝关节磁共振图像(MRI)中分割出膝软骨,提出一种基于多特征支持向量机(SVM)边缘定位和弹性区域生长的自动分割算法。首先,采用自适应Canny边缘检测算法提取图像主要边缘;再对边缘提取多个图像特征,结合SVM算法对边缘进行分类,完成软骨边缘定位;然后,在软骨边缘的基础上进行种子点及软骨像素区域的选择;之后基于选择的结果采用弹性区域生长进行初步软骨分割;最后,基于先验知识和形态学获得最终膝软骨分割结果。实验结果表明:该算法能够准确、快速地自动分割出膝关节MRI中不同的膝软骨,其中股软骨、胫软骨、髌软骨的平均评价重要指标(DSC)分别可达0.8543、0.8280、0.8703,与手工分割结果具有较高的一致性。

关键词: 信息处理技术, 多特征支持向量机, 弹性区域生长, 膝软骨自动分割

Abstract: We present an algorithm that automatically and accurately segments the cartilages from Magnetic Resonance Images (MRI) of knees using multi-feature Support Vector Machine (SVM) and elastic region growing. First, adaptive canny edge detection is used to extract the main edges of the images. Second, the edge features are extracted and the edges are classified with SVM to complete the location of the cartilage edges. Then, based on the cartilage edges, the seeds and regions of the cartilage are chosen, and the preliminary segmentation of knee cartilages is achieved using the elastic region growing. Finally, the priori knowledge and morphology are used to improve the preliminary segmentation results. Experimental results show that the proposed method can accurately and rapidly segment the knee cartilages with high consistency with manual segmentation results. The proposed method obtains average DSC values of 0.8543, 0.8280 and 0.8703 for the femoral, tibial and patellar cartilages.

Key words: information processing technology, multi-feature support vector machine, elastic region growing, automatic segmentation of knee cartilages

中图分类号: 

  • TN911.73
[1] Hani A F M, Kumar D, Malik A S, et al. Multinuclear MR and multilevel data processing: an insight into morphologic assessment of in vivo nnee articular cartilage[J]. Academic Radiology, 2015, 22(1): 93-104.
[2] Peterfy C G, Guermazi A, Zaim S, et al. Whole-organ magnetic resonance imaging score (WORMS) of the knee in osteoarthritis[J]. Osteoarthritis & Cartilage, 2004, 12(3): 177-190.
[3] Hani A F M, Kumar D, Malik A S, et al. Non-invasive and in vivo assessment of osteoarthritic articular cartilage: a review on MRI investigations[J]. Rheumatology International, 2015, 35(1):1-16.
[4] Hani A F M. Fusion of multinuclear magnetic resonance images of knee for the assessment of articular cartilage[C]∥International Conference of the IEEE Engineering in Medicine and Biology Society,Osaka, 2013:6466-6469.
[5] Jaremko J L, Cheng R W, Lambert R G, et al. Reliability of an efficient MRI-based method for estimation of knee cartilage volume using surface registration[J]. Osteoarthritis & Cartilage, 2006, 14(9): 914-922.
[6] König L, Groher M, Keil A, et al. Semi-automatic segmentation of the patellar cartilage in MRI[J].Informatik Aktuell, 2007,25(27):404-408.
[7] Swanson M S, Prescott J W, Best T M, et al. Semi-automated segmentation to assess the lateral meniscus in normal and osteoarthritic knees[J]. Osteoarthritis & Cartilage, 2010, 18(3): 344-353.
[8] Shim H, Chang S, Tao C, et al. Knee cartilage: efficient and reproducible segmentation on high-spatial-resolution MR images with the semiautomated graph-cut algorithm method[J]. Radiology, 2009, 251(2): 548-556.
[9] Lee J G, Gumus S, Moon C H, et al. Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method[J]. Medical Physics, 2014, 41(9): 092303.
[10] González G, Escalante-Ramírez B. Knee cartilage segmentation using active shape models and local binary patterns[J]. Optics Photonics & Digital Technologies for Multimedia Applications III, 2014, 9138: 91380K.
[11] Parsoon A, Petersen K, Lgel C, et al. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network[J]. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2013, 8150: 246-253.
[12] Parsoon A, Petersen K, Lgel C, et al. Femoral cartilage segmentation in knee MRI scans using two stage voxel classification[C]∥Conf Proc IEEE Eng Med Biol Soc, Osaka, 2013: 5469-5472.
[13] 庞剑飞, 邱明国, 陈伟,等. 基于边缘检测与支持向量机的关节软骨自动分割算法研究[J]. 第三军医大学学报, 2013, 35(16): 1653-1657.
Pang Jian-fei, Qiu Ming-guo, Chen Wei, et al. Segmenting articular cartilage automatically by edge detection and support vector machine[J]. J Third Mil Med Univ, 2013, 35(16): 1653-1657.
[14] Pang J, Li P Y, Qiu M, et al. Automatic articular cartilage segmentation based on pattern recognition from knee MRI images[J]. Journal of Digital Imaging, 2015,28(6):695-703.
[15] Liu H, Jezek K C. Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods[J]. International Journal of Remote Sensing, 2010, 25(5): 937-958.
[16] Gonzalez R C,Woods R E. Digital Image Processing[M]. 3rd ed. US: Prentice Hall, 2009: 784-788.
[17] Theodoridis S, Koutroumbas K. Pattern Recognition[M]. 4th ed. Greece: Elsevier, 2009: 134-137.
[18] Hsu C W, Lin C J. A comparison of methods for multi-class support vector machines[J]. IEEE Transactions on Neural Networks, 2002, 13(2): 415-425.
[19] Hojjatoleslami S A, Kittler J. Region growing:a new approach[J]. IEEE Transactions on Image Processing, 1998, 7(7): 1079-1084.
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