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Diabetic retinopathy(DR) is a health condition that affects the retinal blood vessels(BV) and arises in over half of people living with diabetes. Exudates(EX) are significant indications of DR. Early detection and treatment can prevent vision loss in many cases. EX detection is a challenging problem for ophthalmologists due to its different sizes and elevations as retinal fundus images frequently have irregular illumination and are poorly contrasting. Manual detection of EX is a time-consuming process to diagnose a mass number of diabetic patients. In the domain of signal processing, both SIFT (scale-invariant feature transform) and SURF (speed-up robust feature) methods are predominant in scale-invariant location retrieval and have shown a range of advantages. But, when extended to medical images with corresponding weak contrast between reference features and neighboring areas, these methods cannot differentiate significant features. Considering these, in this paper, a novel method is proposed based on modified KAZE features, which is an emerging technique to extract feature points and extreme learning machine autoencoders(ELMAE) for robust and fast localization of the EX in fundus images. The main stages of the proposed method are pre-processing, OD localization, dimensionality reduction using ELMAE, and EX localization. The proposed method is evaluated based on the freely accessible retinal database DIARETDB0, DIARETDB1, e-Ophtha, MESSIDOR, and local retinal database collected from Silchar Medical College and Hospital(SMCH). The sensitivity, specificity, and accuracy obtained by the proposed method are 96.5%, 96.4%, and 97%, respectively, with the processing time of 3.19 seconds per image. The results of this study are satisfactory with state-of-the-art methods. The results indicate that the approach taken can detect EX with less processing time and accurately from the fundus images. [ABSTRACT FROM AUTHOR]

In this study we estimated the extent of exudativory in Nycticebus bengalensis and examined whether exudates can be considered as fallback foods. This study was carried out in Trishna Wildlife Sanctuary, northeastern India, in winter (December–February) and summer (March and April). We estimated time–activity budget using instantaneous sampling and used continuous focal animal sampling to record all instances and durations of feeding, over a total of 177 hr. Feeding accounted for 22.3±2.2% of the activity budget, with no seasonal difference. Bengal slow lorises fed on exudates, nectar, fruit, bark, invertebrates and avian eggs. In addition to scraping they also obtained exudates by gouging holes into the bark of trees. In winter, lorises almost exclusively fed on exudates (94.3% of winter feeding time). In summer, exudates (67.3%) and nectar from one species (22.3%) dominated the diet. This study identifies the Bengal slow loris as the most exudativorous loris. Exudates rather than being a staple fallback food, seem to be a preferred, patchily distributed and common food in the diet of the Bengal slow loris. Exudativory in this species is characterized by high selectivity among species and seasonal variation, which may be related to variations in productivity of exudates and their chemical composition. An understanding of these factors is necessary for predicting the response of this species to human disturbance such as logging. This study also underscores the importance of protecting some of the common species such as Terminalia belerica on which the loris feeds during periods of scarcity. Am. J. Primatol. 72:113–121, 2010. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]