Comput Methods Programs Biomed - Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.


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Diabetic retinopathy (DR) is one of the most important complications of diabetes mellitus, which causes serious damages in the retina, consequently visual loss and sometimes blindness if necessary medical treatment is not applied on time. One of the difficulties in this illness is that the patient with diabetes mellitus requires a continuous screening for early detection. So far, numerous methods have been proposed by researchers to automate the detection process of DR in retinal fundus images. In this paper, we developed an alternative simple approach to detect DR. This method was built on the inverse segmentation method, which we suggested before to detect Age Related Macular Degeneration (ARMDs). Background image approach along with inverse segmentation is employed to measure and follow up the degenerations in retinal fundus images. Direct segmentation techniques generate unsatisfactory results in some cases. This is because of the fact that the texture of unhealthy areas such as DR is not homogenous. The inverse method is proposed to exploit the homogeneity of healthy areas rather than dealing with varying structure of unhealthy areas for segmenting bright lesions (hard exudates and cotton wool spots). On the other hand, the background image, dividing the retinal image into high and low intensity areas, is exploited in segmentation of hard exudates and cotton wool spots, and microaneurysms (MAs) and hemorrhages (HEMs), separately. Therefore, a complete segmentation system is developed for segmenting DR, including hard exudates, cotton wool spots, MAs, and HEMs. This application is able to measure total changes across the whole retinal image. Hence, retinal images that belong to the same patients are examined in order to monitor the trend of the illness. To make a comparison with other methods, a Na?ve Bayes method is applied for segmentation of DR. The performance of the system, tested on different data sets including various qualities of retinal fundus images, is over 95% in detection of the optic disc (OD), and 90% in segmentation of the DR.

Resumo Limpo

diabet retinopathi dr one import complic diabet mellitus caus serious damag retina consequ visual loss sometim blind necessari medic treatment appli time one difficulti ill patient diabet mellitus requir continu screen earli detect far numer method propos research autom detect process dr retin fundus imag paper develop altern simpl approach detect dr method built invers segment method suggest detect age relat macular degener armd background imag approach along invers segment employ measur follow degener retin fundus imag direct segment techniqu generat unsatisfactori result case fact textur unhealthi area dr homogen invers method propos exploit homogen healthi area rather deal vari structur unhealthi area segment bright lesion hard exud cotton wool spot hand background imag divid retin imag high low intens area exploit segment hard exud cotton wool spot microaneurysm mas hemorrhag hem separ therefor complet segment system develop segment dr includ hard exud cotton wool spot mas hem applic abl measur total chang across whole retin imag henc retin imag belong patient examin order monitor trend ill make comparison method nave bay method appli segment dr perform system test differ data set includ various qualiti retin fundus imag detect optic disc od segment dr

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