Abstract
The task of detecting peri-papillary indicators associated with the glaucoma is challenging due to the high degree of intra-and inter-image variations commonly seen in colour retinal images. The existing approaches based on direct modeling of the region of interest fail to handle such image variations which compromises detection accuracy. In this paper, a novel detection strategy is proposed which exploits the saliency property associated with these indicators. The region of interest is modeled as a region substantially different from the adjacent image regions. This dissimilarity information is derived at the feature level, between the target and its adjacent regions. Based on the proposed strategy, two novel methods are presented for the detection of peri-papillary atrophy and RNFL defect from colour retinal images. Two different datasets have been used to evaluate the performance of developed solutions. The obtained results are encouraging and establish the strength of the proposed strategy in handling high degree of image variations. The preliminary results and comparative evaluation with direct modeling strategy show viability of proposed strategy to be used in the glaucoma assessment task.
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Joshi, G.D., Sivaswamy, J., Prashanth, R., Krishnadas, S.R. (2012). Detection of Peri-Papillary Atrophy and RNFL Defect from Retinal Images. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2012. Lecture Notes in Computer Science, vol 7325. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31298-4_47
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DOI: https://doi.org/10.1007/978-3-642-31298-4_47
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