Superpixel Classification for Initialization in Model Based Optic Disc Segmentation

Superpixel Classification for Initialization in Model Based Optic Disc Segmentation
Title:
Superpixel Classification for Initialization in Model Based Optic Disc Segmentation
Other Titles:
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Keywords:
Publication Date:
28 August 2012
Citation:
Jun Cheng; Jiang Liu; Yanwu Xu; Fengshou Yin; Wong, D.W.K.; Beng-Hai Lee; Cheung, C.; Tin Aung; Tien Yin Wong, "Superpixel classification for initialization in model based optic disc segmentation," in Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE , vol., no., pp.1450-1453, Aug. 28 2012-Sept. 1 2012
Abstract:
Optic disc segmentation in retinal fundus image is important in ocular image analysis and computer aided diagnosis. Because of the presence of peripapillary atrophy which affects the deformation, it is important to have a good initialization in deformable model based optic disc segmentation. In this paper, a superpixel classification based method is proposed for the initialization. It uses histogram of superpixels from the contrast enhanced image as features. In the training, 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 initialization and the segmentation. The proposed method has been tested in a database of 650 images with optic disc boundaries marked by trained professionals manually. The experimental results show an mean overlapping error of 10.0% and standard deviation of 7.5% in the best scenario. The results also show an increase in overlapping error as the reliability score reduces, which justifies the effectiveness of the self-assessment. The method can be used for optic disc boundary initialization and segmentation in computer aided diagnosis system and the self-assessment can be used as an indicator of cases with large errors and thus enhance the usage of the automatic segmentation.
License type:
PublisherCopyrights
Funding Info:
Description:
ISSN:
1557-170X
ISBN:
978-1-4244-4119-8
978-1-4577-1787-1
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