Comput Methods Programs Biomed - Obtaining optic disc center and pixel region by automatic thresholding methods on morphologically processed fundus images.


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Development of automatic retinal disease diagnosis systems based on retinal image computer analysis can provide remarkably quicker screening programs for early detection. Such systems are mainly focused on the detection of the earliest ophthalmic signs of illness and require previous identification of fundal landmark features such as optic disc (OD), fovea or blood vessels. A methodology for accurate center-position location and OD retinal region segmentation on digital fundus images is presented in this paper. The methodology performs a set of iterative opening-closing morphological operations on the original retinography intensity channel to produce a bright region-enhanced image. Taking blood vessel confluence at the OD into account, a 2-step automatic thresholding procedure is then applied to obtain a reduced region of interest, where the center and the OD pixel region are finally obtained by performing the circular Hough transform on a set of OD boundary candidates generated through the application of the Prewitt edge detector. The methodology was evaluated on 1200 and 1748 fundus images from the publicly available MESSIDOR and MESSIDOR-2 databases, acquired from diabetic patients and thus being clinical cases of interest within the framework of automated diagnosis of retinal diseases associated to diabetes mellitus. This methodology proved highly accurate in OD-center location: average Euclidean distance between the methodology-provided and actual OD-center position was 6.08, 9.22 and 9.72 pixels for retinas of 910, 1380 and 1455 pixels in size, respectively. On the other hand, OD segmentation evaluation was performed in terms of Jaccard and Dice coefficients, as well as the mean average distance between estimated and actual OD boundaries. Comparison with the results reported by other reviewed OD segmentation methodologies shows our proposal renders better overall performance. Its effectiveness and robustness make this proposed automated OD location and segmentation method a suitable tool to be integrated into a complete prescreening system for early diagnosis of retinal diseases.

Resumo Limpo

develop automat retin diseas diagnosi system base retin imag comput analysi can provid remark quicker screen program earli detect system main focus detect earliest ophthalm sign ill requir previous identif fundal landmark featur optic disc od fovea blood vessel methodolog accur centerposit locat od retin region segment digit fundus imag present paper methodolog perform set iter openingclos morpholog oper origin retinographi intens channel produc bright regionenhanc imag take blood vessel confluenc od account step automat threshold procedur appli obtain reduc region interest center od pixel region final obtain perform circular hough transform set od boundari candid generat applic prewitt edg detector methodolog evalu fundus imag public avail messidor messidor databas acquir diabet patient thus clinic case interest within framework autom diagnosi retin diseas associ diabet mellitus methodolog prove high accur odcent locat averag euclidean distanc methodologyprovid actual odcent posit pixel retina pixel size respect hand od segment evalu perform term jaccard dice coeffici well mean averag distanc estim actual od boundari comparison result report review od segment methodolog show propos render better overal perform effect robust make propos autom od locat segment method suitabl tool integr complet prescreen system earli diagnosi retin diseas

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