jiang-mappingfunctions-2018.pdf (7.47 MB)
Mapping functions driven robust retinal vessel segmentation via training patches
journal contribution
posted on 2018-09-12, 00:00 authored by H Xia, Frank JiangFrank Jiang, S Deng, J Xin, Robin Ram Mohan DossRobin Ram Mohan Doss© 2013 IEEE. Vein occlusions and diabetic retinopathy are two of many retinal pathologies affecting the retina. Understanding robust vessel segmentation of fundus images is of vital importance for improving the diagnosis results of these diseases. This paper proposes a novel approach for computing the minimum distance for each test patch via the distance comparison within the test patch and cluster centers. The numerous patches are calculated using manual segmentations through the K-means algorithm. We demonstrate the efficiency of learning the simple pattern from each cluster; meanwhile, the mapping function for each cluster is determined by the patches in the training images and their corresponding manual segmentation patches. Two publicly recognized benchmark data sets, namely DRIVE and STARE, are used in our experimental validation. Experimental results show that the proposed approach outperforms conventional methods for vessel segmentation problems validated via public benchmark data sets, i.e., DRIVE and STARE.
History
Journal
IEEE accessVolume
6Pagination
61973 - 61982Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
Link to full text
eISSN
2169-3536Language
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2018, IEEEUsage metrics
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Keywords
Science & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringFundus imagesmapping functionsvessel segmentationBLOOD-VESSELSIMAGESGABORInformation SystemsArtificial Intelligence and Image ProcessingDistributed Computing
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