A new approach for texture segmentation based on the Gray Level Co-occurrence Matrix

Image processing is a very rich and important research area, which provides efficient solutions to many real and industrial problems. Texture analysis is one of the most interesting fields in image processing and pattern recognition. It became a very attractive research area these last years, especially after the growth and the advancement of technologies. This paper deals with texture analysis and unsupervised texture segmentation problem. The goal of this study is to develop a new segmentation method based on the textural features of the images. The proposed system is composed of different steps. First, the image is analyzed in each pixel using the Gray Level Co-occurrence Matrix (GLCM) feature extraction method. Four Haralick parameters (Haralick Proc IEEE 67(5):786–804, 16 ) are calculated and represented in four matrix. After that, we applied the gradient to detect edges from the extracted images. In order to localize the area of the discontinuity of the texture, we proposed a new method for joining the edge and region growing. The proposed system is applied on several textured images and the obtained results are shown in the experimentation section. A number of experiments have been done with randomly generated textured images. The experiments have shown the efficiency of the proposed method compared to other existing methods and its robustness to enhance the segmentation precision of textured images.