Cheng Jun, Liu Jiang, Xu Yanwu, Yin Fengshou, Wong Damon Wing Kee, Lee Beng-Hai, Cheung Carol, Aung Tin, Wong Tien Yin
Institute for Infocomm Research, A*Star, Singapore.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1450-3. doi: 10.1109/EMBC.2012.6346213.
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.
视网膜眼底图像中的视盘分割在眼部图像分析和计算机辅助诊断中具有重要意义。由于存在影响变形的视乳头周围萎缩，在基于可变形模型的视盘分割中进行良好的初始化非常重要。本文提出了一种基于超像素分类的初始化方法。它使用对比度增强图像中超像素的直方图作为特征。在训练中，采用自训练来处理由于视乳头周围萎缩的存在而导致的不平衡聚类问题。计算一个自我评估可靠性分数来评估初始化和分割的质量。所提出的方法已在一个包含650张图像的数据库中进行了测试，这些图像的视盘边界由训练有素的专业人员手动标记。实验结果表明，在最佳情况下，平均重叠误差为10.0%，标准差为7.5%。结果还表明，随着可靠性分数的降低，重叠误差会增加，这证明了自我评估的有效性。该方法可用于计算机辅助诊断系统中的视盘边界初始化和分割，自我评估可作为大误差病例的指标，从而提高自动分割的使用率。
