Here we propose a novel method to analysis the glaucoma in medical image. To map ganglion cell complex (GCC) thickness with high-speed Fourier-domain optical coherence tomography (FD-OCT) and compute novel macular parameters for glaucoma diagnosis. In pre-Processing we separate red, green, blue color channel from the retinal images. The green channel will pass to the further process. The green color plane was used in the analysis since it shows the best contrast between the vessels and the background retina. Here we identify the thickness of the macular retinal (MR) thickness and GCC thickness. Pattern analysis was applied to the GCC map and the diagnostic powers of pattern-based diagnostic parameters were investigated. Here we will use the Fourier-Domain Optical Coherence Tomography (FD-OCT). (FD-OCT) is used for the macula segmentation. For classification we extract the GLCM feature. In the GLCMs, several statistics information is derived using the different formulas. These statistics provide information about the texture of an image. Then we classify the result by SVM classification. This is one of the Kernel-based techniques which represent a major development in machine learning algorithms. We will provide our feature values to the SVM classifier. The classifier will train about the feature. Finally it will classify about the result.