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Superpixel classification based optic disc segmentation

Publication ,  Conference
Cheng, J; Liu, J; Xu, Y; Yin, F; Wong, DWK; Tan, NM; Cheng, CY; Tham, YC; Wong, TY
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
April 11, 2013

Optic disc segmentation in retinal fundus images is important in computer aided diagnosis. In this paper, an optic disc segmentation method based on superpixel classification is proposed. In the classification, histograms from contrast enhanced image channels and center surround statistics from center surround difference maps are proposed as features to determine each superpixel as disc or non disc. In the training step, bootstrapping is adopted to handle the unbalanced cluster issue due to the presence of peripapillary atrophy. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. The proposed method has been tested on a database of 650 images with optic disc boundaries marked by trained professionals manually. The experimental results show a mean overlapping error of 9.5%, better than previous methods. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The method can be used in computer aided diagnosis systems and the self-assessment can be used as an indicator of results with large errors and thus enhance the clinical deployment of the automatic segmentation. © 2013 Springer-Verlag.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

April 11, 2013

Volume

7725 LNCS

Issue

PART 2

Start / End Page

293 / 304

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D. W. K., Tan, N. M., … Wong, T. Y. (2013). Superpixel classification based optic disc segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7725 LNCS, pp. 293–304). https://doi.org/10.1007/978-3-642-37444-9_23
Cheng, J., J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N. M. Tan, C. Y. Cheng, Y. C. Tham, and T. Y. Wong. “Superpixel classification based optic disc segmentation.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7725 LNCS:293–304, 2013. https://doi.org/10.1007/978-3-642-37444-9_23.
Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan NM, et al. Superpixel classification based optic disc segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013. p. 293–304.
Cheng, J., et al. “Superpixel classification based optic disc segmentation.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7725 LNCS, no. PART 2, 2013, pp. 293–304. Scopus, doi:10.1007/978-3-642-37444-9_23.
Cheng J, Liu J, Xu Y, Yin F, Wong DWK, Tan NM, Cheng CY, Tham YC, Wong TY. Superpixel classification based optic disc segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2013. p. 293–304.

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

Publication Date

April 11, 2013

Volume

7725 LNCS

Issue

PART 2

Start / End Page

293 / 304

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences