Diabetic retinopathy is the most common cause of blindness in population in developed countries. However, with early diagnosis of this asymptomatic disease, it could be prevented 80 percent of cases. Diabetic retinopathy is detected in fundus images, also called retinal digital angiography. In most cases, due to the concave geometry of the eye, these images have a high variability in local contrast and luminance. This lack of uniformity may mask signs of this disease as ocular hemorrhages, microaneurysms, hard exudates and cotton wool spots affecting the diagnostic quality. This paper presents an automatic method for the segmentation of hard exudates and cotton wool in fundus images based on Mathematical Morphology in color spaces. The aim of this development is to assist the expert in the diagnosis of disease. To validate the proposed method, the Diabetic Retinopathy Database and the Evaluation Protocol diaRetdB0 and diaRetdB1 were used. The proposed method archived values above 96.5%. This performance was compared with a technique developed by other authors, obtaining a difference of above 26.5% of true positives in favor of our method.
There are many different methods to perform gray level segmentation in microscope cell images, however in some
circumstances texture features and roughness are not as relevant as the color for the segmentation task. In many
biomedical applications, where these types of images are analyzed, the aim is to segment nuclei for their clinical
analysis. In this work, a fuzzy color mathematical morphology reconstruction technique was developed, based on a new
locally defined ordering, to achieve microscope cell images segmentation. We show experimental results for this
proposed fuzzy color morphological reconstruction, which show that this tool can be efficiently applied in cells
segmentation without generating false colors.
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