ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Medical image segmentation algorithm based on dictionary learning and sparse clustering

  • Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230027, China

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https://doi.org/10.3969/j.issn.0253-2778.2019.10.003
  • Received Date: 30 April 2018
  • Accepted Date: 18 March 2019
  • Rev Recd Date: 18 March 2019
  • Publish Date: 31 October 2019
    Abstract

  • Abstract

    To improve the segmentation performance of medical images, dictionary learning was combined with clustering algorithm, and a medical image segmentation algorithm was proposed taking dictionaries as clustering centers and using sparse representation to cluster for segmentation. For a single medical image, unsupervised adaptive segmentation can be achieved by alternately iterating the sparse coding and updating the dictionary to convergence. For the medical image sequence, the sample images can be picked to obtain the trained dictionaries to complete the segmentation of the image sequence. According to the segmentation results of the synthetic images and the magnetic resonance images of the human brain from SBD database, it can be perceived that the proposed algorithm could not only improve segmentation accuracy, but also maintain the accuracy and consistency of sequential medical image segmentation.

    Abstract

    To improve the segmentation performance of medical images, dictionary learning was combined with clustering algorithm, and a medical image segmentation algorithm was proposed taking dictionaries as clustering centers and using sparse representation to cluster for segmentation. For a single medical image, unsupervised adaptive segmentation can be achieved by alternately iterating the sparse coding and updating the dictionary to convergence. For the medical image sequence, the sample images can be picked to obtain the trained dictionaries to complete the segmentation of the image sequence. According to the segmentation results of the synthetic images and the magnetic resonance images of the human brain from SBD database, it can be perceived that the proposed algorithm could not only improve segmentation accuracy, but also maintain the accuracy and consistency of sequential medical image segmentation.
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    • References(16)
  • [1]
    聂生东, 邱建峰, 郑建立. 医学图像处理[M]. 上海：复旦大学出版社, 2010.
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    HAZRA J, CHOWDHURY A R, DUTTA P. Cluster Based Medical Image Registration Using Optimized Neural Network[M]// Medical Imaging: Concepts, Methodologies, Tools, and Applications，2016.
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    DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1973, 3(3): 32-57.
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    AHMED M N, YAMANY S M, MOHAMEDN, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199.
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    KRINIDIS S, CHATZISV. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337.
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    ENGAN K, AASE S O, HUSOY J H. Method of optimal directions for frame design[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing.Barcelona, Spain:IEEE, 1999, 5: 2443-2446.
    [7]
    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.
    [8]
    TOSIC I, FROSSARD P. Dictionary learning[J]. IEEE Signal Processing Magazine, 2011, 28(2): 27-38.
    [9]
    MACQUEEN J. Some methods for classification and analysis of multivariate observations[J].Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. 1967, 1(14): 281-297.
    [10]
    MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on signal processing, 1993, 41(12): 3397-3415.
    [11]
    BrainWeb: Simulated Brain Database[EB/OL]. [2018-03-18]http://www.bic.mni.mcgill.ca/brainweb/.
    [12]
    COCOSCO C A, KOLLOKIAN V, KWAN R K S, et al. BrainWeb: Online interface to a 3D MRI simulated brain database[J]. NeuroImage, 1997, 5(4):S425.
    [13]
    CHEN S, ZHANG D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(4): 1907-1916.
    [14]
    CHOUDHRY M S, KAPOOR R. Performance analysis of fuzzy C-means clustering methods for MRI image segmentation[J]. Procedia Computer Science, 2016, 89:749-758.
    [15]
    JACCARD P. The distribution of the flora in the alpine zone[J]. New Phytologist, 2010, 11(2):37-50.
    [16]
    TAHA A A, HANBURY A. Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool[J]. BMC Medical Imaging, 2015, 15(1):29-56.)
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    [1]
    聂生东, 邱建峰, 郑建立. 医学图像处理[M]. 上海：复旦大学出版社, 2010.
    [2]
    HAZRA J, CHOWDHURY A R, DUTTA P. Cluster Based Medical Image Registration Using Optimized Neural Network[M]// Medical Imaging: Concepts, Methodologies, Tools, and Applications，2016.
    [3]
    DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters[J]. Journal of Cybernetics, 1973, 3(3): 32-57.
    [4]
    AHMED M N, YAMANY S M, MOHAMEDN, et al. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data[J]. IEEE Transactions on Medical Imaging, 2002, 21(3): 193-199.
    [5]
    KRINIDIS S, CHATZISV. A robust fuzzy local information C-means clustering algorithm[J]. IEEE Transactions on Image Processing, 2010, 19(5): 1328-1337.
    [6]
    ENGAN K, AASE S O, HUSOY J H. Method of optimal directions for frame design[C]//Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing.Barcelona, Spain:IEEE, 1999, 5: 2443-2446.
    [7]
    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.
    [8]
    TOSIC I, FROSSARD P. Dictionary learning[J]. IEEE Signal Processing Magazine, 2011, 28(2): 27-38.
    [9]
    MACQUEEN J. Some methods for classification and analysis of multivariate observations[J].Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. 1967, 1(14): 281-297.
    [10]
    MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on signal processing, 1993, 41(12): 3397-3415.
    [11]
    BrainWeb: Simulated Brain Database[EB/OL]. [2018-03-18]http://www.bic.mni.mcgill.ca/brainweb/.
    [12]
    COCOSCO C A, KOLLOKIAN V, KWAN R K S, et al. BrainWeb: Online interface to a 3D MRI simulated brain database[J]. NeuroImage, 1997, 5(4):S425.
    [13]
    CHEN S, ZHANG D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(4): 1907-1916.
    [14]
    CHOUDHRY M S, KAPOOR R. Performance analysis of fuzzy C-means clustering methods for MRI image segmentation[J]. Procedia Computer Science, 2016, 89:749-758.
    [15]
    JACCARD P. The distribution of the flora in the alpine zone[J]. New Phytologist, 2010, 11(2):37-50.
    [16]
    TAHA A A, HANBURY A. Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool[J]. BMC Medical Imaging, 2015, 15(1):29-56.)
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