Texture is the most important visual cue in identifying homogeneous regions. This is called texture classification. The goal of texture classification then is to produce a classification map of the input image where each uniform textured region is identified with the texture class it belongs. We could also find the texture boundaries even if we could not classify these textured surfaces. This is then the second type of problem that texture analysis research attempts to solve-texture segmentation. The texture features (texture elements) are distorted due to the imaging process and the perspective projection which provide information about surface orientation and shape. In general, the applications involve the automatic extraction of features from the image which is then used for a variety of classification tasks, such as distinguishing normal tissue from abnormal tissue. In this paper, SGLDM, GLRLM and RGLRLM are used to extract the features from the segmented image.RGLRLM method is based on GLRLM, and the idea is to calculate the run length from the intensity value 100, not from the 0. And we have clearly explained the method difference, and also helpful to the physician for interpretation. © 2017 Elsevier B.V., All rights reserved.