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A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.

This paper proposes a novel Adaptive Regionbased Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region C...

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Published in:Journal of Medical Systems Vol. 42; no. 1; pp. 19 - 37
Main Authors: Haleem, Muhammad Salman, Liangxiu Han, van Hemert, Jano, Baihua Li, Fleming, Alan, Pasquale, Louis R., Song, Brian J.
Format: algorithm equations & formulas pictorial tables/charts Journal Article
Published: Springer Nature 12/7/2017
Online Access:View this record in EBSCOhost
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      dt: 12/7/2017
      vid: 42
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10916-017-0859-4
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        atl: A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis.
      aug:
        au:
          Haleem, Muhammad Salman
          Liangxiu Han
          van Hemert, Jano
          Baihua Li
          Fleming, Alan
          Pasquale, Louis R.
          Song, Brian J.
        affil: School of Computing, Mathematics and Digital Technology, Manchester Metropolitan University, Manchester M1 5GD, UK
      sug:
        subj:
          Glaucoma Diagnosis
          Diagnosis, Computer Assisted
          Image Processing, Computer Assisted
          Optical Disks
          Human
          P-Value
          ROC Curve
          Paired T-Tests
          Funding Source
      ab: This paper proposes a novel Adaptive Regionbased Edge Smoothing Model (ARESM) for automatic boundary detection of optic disc and cup to aid automatic glaucoma diagnosis. The novelty of our approach consists of two aspects: 1) automatic detection of initial optimum object boundary based on a Region Classification Model (RCM) in a pixel-level multidimensional feature space; 2) an Adaptive Edge Smoothing Update model (AESU) of contour points (e.g. misclassified or irregular points) based on iterative force field calculations with contours obtained from the RCM by minimising energy function (an approach that does not require predefined geometric templates to guide auto-segmentation). Such an approach provides robustness in capturing a range of variations and shapes. We have conducted a comprehensive comparison between our approach and the state-of-the-art existing deformable models and validated it with publicly available datasets. The experimental evaluation shows that the proposed approach significantly outperforms existing methods. The generality of the proposed approach will enable segmentation and detection of other object boundaries and provide added value in the field of medical image processing and analysis.
      pubtype: Academic Journal
      doctype:
        algorithm
        equations & formulas
        pictorial
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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