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Hip-Joint CT Image Segmentation Based on Hidden Markov Model with Gauss Regression Constraints.

Hip-joint CT images have low organizational contrast, irregular shape of boundaries and image noises. Traditional segmentation algorithms often require manual intervention or introduction of some prior information, which results in low efficiency and is unable to meet clinical needs. In order to ove...

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Published in:Journal of Medical Systems Vol. 43; no. 10
Main Authors: Liu, Haiyang, Dai, Guochao, Pu, Fushun
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Oct2019
Online Access:View this record in EBSCOhost
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      dt: Oct2019
      vid: 43
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      pub: Springer Nature
      place: New York, New York
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        atl: Hip-Joint CT Image Segmentation Based on Hidden Markov Model with Gauss Regression Constraints.
      aug:
        au:
          Liu, Haiyang
          Dai, Guochao
          Pu, Fushun
        affil: Department of Radiology, Shangluo Central Hospital, 726000, Shangluo, Shaanxi, China
      sug:
        subj:
          Hip Joint
          Tomography, X-Ray Computed
          Image Processing, Computer Assisted
          Hidden Markov Models
          Algorithms
          Contrast Media
          Probability
          Image Enhancement
      ab: Hip-joint CT images have low organizational contrast, irregular shape of boundaries and image noises. Traditional segmentation algorithms often require manual intervention or introduction of some prior information, which results in low efficiency and is unable to meet clinical needs. In order to overcome the sensitivity of classical fuzzy clustering image segmentation algorithm to image noise, this paper proposes a fuzzy clustering image segmentation algorithm combining Gaussian regression model (GRM) and hidden Markov random field (HMRF). The algorithm uses the prior information to regularize the objective function of the fuzzy C-means, and then improves it with KL information. The HMRF model establishes the neighborhood relationship of the label field by prior probability, while CRM model establishes the neighborhood relationship of feature field on the basis of the consistency between the central pixel label and its neighborhood pixel label. The experimental results show that the proposed algorithm has high segmentation accuracy.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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