Analysis of normal human eye with different age groups using infrared images.

The human body temperature is a good health indicator. All objects emit thermal radiation as a function temperature and wavelength for all wavelengths. The wavelength of infrared rays lies between visible and microwave radiations ranging between 700 nm to 0.1 mm. Infrared (IR) imaging is relatively inexpensive, noninvasive and harmless. Nowadays, it is widely used in the medical field for diagnosis. In this work, we have applied image processing techniques on the IR images of the eye for the analysis of the ocular surface temperature (OST) of the normal subjects of three categories (young, middle and old ages). In our study, 67 IR normal images were analyzed. Two parameters, average ocular temperature and the temperature deviation were proposed to study the variability of OST in different normal category subjects. Our study shows that, the two parameters proposed, show distinct ranges for different groups with 'p' values less than 0.05.

Automated identification of diabetic retinopathy stages using digital fundus images.

Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p < 0.0001) with distinct mean +/- SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.