Assessment of Glaucoma with ocular thermal images using GLCM techniques and Logistic Regression classifier

In this paper we propose a methodology for early detection and recognition of Glaucoma in ocular thermographs. Ocular thermography is an efficient tool not only to capture temperatures of corneal surface, but also to detect and visualize any changes on the Ocular surface temperature. The proposed method uses a linear transformation for pre-processing. Logistic Regression based classifier with the features collected from GLCM is used to classify the given ocular IR thermal image into Glaucoma from the normal eye. The efficacy of the proposed technique is proved over a number of ocular thermal image samples.