SUMMARY
Ocular thermography is an important, emerging modality in the diagnosis and management of diseases related to eye. It is a non-invasive procedure to evaluate the presence of eye diseases and monitor the response to treatments. In this paper, we propose and evaluate a system designed using infrared thermal image processing that detects glaucoma. Euclidean distance based segmentation technique is used to threshold the IR image to obtain the region of interest, where the manifestation of glaucoma is predominant. Features are extracted using statistical moments from the temperature mapped IR image and Gray Level Co-Occurrence Matrix of the IR image. Two significant attributes, namely the homogeneity and area of region of interest are the inputs to a Support Vector Machine classifier to classify a given input ocular thermal image as a normal or diseased image. In our simulation study, one hundred ocular thermal images with even number of normal and diseased subjects were analysed. The classifier has achieved a maximum accuracy of 96% when homogeneity and area of region of interest are used as attributes, indicating the potential use of proposed method for screening patients even at early stages of glaucoma.