The GLCM is a tabulation of how often different combinations of pixel brightness values (grey levels) occur in an image Through GLCM features we can extract the textural features of a medical image. GLCM method always focuses on the pixel intensity level of the neighboring pixel [6]. GLCM always accounts for the specific position of pixel relative to other pixel. This is a simple tabulation that shows how often the different combinations of pixel brightness values occur in medical image. By taking d values as d=l, 2, 3, 4 for the direction of data given for 0°, 45°, 90°, 180° matrices of GLCM was constructed [9].P(i,j) represents the probability that 2 pixels with the specified separation have gray levels i and j. GLCM defines the probability of the occurrence of the combination of one grey tone at a specified distance and in a direction .The RGB image first get converted into gray scale image and given as input to the GLCM, in which the number of rows and columns is equal to the number of gray levels GLCM computes the frequency of certain number of gray levels reappearing at different position in an image[9]. GLCM feature extraction maps the gray level co-occurrence probabilities in different angular position based on spatial relationship between different combination of pixels. The following features are extracted from matrix based on GLCM: autocorrelation, Contrast, Energy, Entropy, Homogeneity A gray level co-occurrence matrix (GLCM) or cooccurrence distribution (less often co-occurrence matrix or co-occurrence distribution) is a matrix or distribution that is defined over an image to be the distribution of co-occurring values at a given offset. A GLCM is a matrix where the number of rows and columns is equal to the number of gray levels, G, in the image The use of statistical features is therefore one of the early methods proposed in the image processing literature. Haralick [14] suggested the use of cooccurrence matrix or gray level co-occurrence matrix. It considers the relationship between two neighboring pixels, the first pixel is known as a reference and the second is known as a neighbor pixel