New analysis tools to address the problem of early detection of the eye blinding disease glaucoma are presented. The thickness maps of the Retinal Nerve Fiber Layer (RNFL) corresponding to 184 eyes (92 Normal and 92 Glaucoma Patients) were obtained from a Scanning Laser Polarimeter (Gdx-VCC). The two dimensional data was used to draw features as opposed to the circular band one-dimensional data in previous approaches. Fourier analysis was performed on the 90° projection of the thickness map data to emphasize the shape contained in the RNFL. Different parameters from the Fourier Coefficients were drawn and tested for their ability to detect glaucoma. Significant differences were found in the shape measures of the projections and the ROC curve analysis was done to measure the separability of the sample set with those features. Another approach was to analyze the shape of the entire 2 dimensional thickness map through a 2D Fourier Transform. A circular ring band (10 pixel wide) data at a radius of 20 pixels was analyzed for this 2D FT. Principal Component Analysis was performed on this data for dimension reduction of feature space. Finally Fisher's linear discriminant function (LDF) was used as a classifier. The evaluation of different parameters obtained through the Fourier analysis of the thickness map image of RNFL was found to be a useful tool as an analysis strategy for glaucoma detection.