Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results ...

Ultrasound applications are used for diagnostic applications such as visualizing muscles, tendons, internal organs, to determine its size, structures, any lesions or other abnormalities. This paper concentrates the diagnosis of abnormalities in kidney Images based on retrieving past similar images from kidney Image Database. More and more amount of ultrasound digital images are being captured a...

Seismic interpretation can be supported by seismic attribute analysis. Common seismic attributes use mathematical relationships based on the geometry and the physical properties of the subsurface to reveal features of interest. But they are mostly not capable of describing the spatial arrangement of depositional facies or reservoir properties. Textural attributes such as the grey level co-occur...

In this paper, images of realworld natural scenes and manmade structures of different depth are taken. With increase in image depth , roughness increases in case of man-made structures whereas natural scene images become smooth, thus reducing the local roughness of the picture. Such kind of specific arrangement produces a particular spatial pattern of dominant orientations and scales that can b...

Texture classification is a problem that has been studied and tested using different methods due to its valuable usage in various pattern recognition problems, such as wood recognition and rock classification. The Grey-level Co-occurrence Matrices (GLCM) and Gabor filters are both popular techniques used on texture classification. This paper combines both techniques in order to increase the acc...

In the last few years, computerized tool play important role in detection of breast cancer. This paper proposes a method for breast cancer diagnosis in digital mammograms using GLCM (Grey Level Co-occurrence Matrix) features. In this paper CAD (Computer Aided Diagnosis) system developed using GLCM feature and neural network. Mammography is an efficient tool for early detection of breast cancer....

GLCM texture features have been widely used to characterize biomedical images. Most of the previous studies using GLCM features to characterize biomedical images only consider single or limited color space due to the use of only one color model. To mimic human color perception, conventional RGB color model may need to be supplemented with other color space models for better human vision represe...

This paper proposes a steganalysis technique for both grayscale and color images. It uses the feature vectors derived from gray level co-occurrence matrix (GLCM) in spatial domain, which is sensitive to data embedding process. This GLCM matrix is derived from an image. Several combinations of diagonal elements of GLCM are considered as features. There is difference between the features of stego...

A method for an anomaly detection system was developed to automate process of recognizing an anomaly of roentgen image by utilizing fuzzy histogram hyperbolization image enhancement and gray level co-occurrence matrix(GLCM). The system consists of image acquisition, pre-processor, feature extractor, response selector and output. Fuzzy Histogram Hyperbolization is chosen to improve the quality o...

Gray level co-occurrence matrix (GLCM) is an important method to extract the image texture features of synthetic aperture radar (SAR). However, GLCM can only extract the textures under single scale and single direction. A kind of texture feature extraction method combining nonsubsampled contour transformation (NSCT) and GLCM is proposed, so as to achieve the extraction of texture features under...