In this study, an efficient feature fusion based technique for the classification of colour texture images in VisTex album is presented. Gray Level Co-occurrence Matrix (GLCM) and its associated texture features contrast, correlation, energy and homogeneity are used in the proposed approach. The proposed GLCM texture features are obtained from the original colour texture as well as the first no...

In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumo...

In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features fro...

OBJECTIVES In this paper, we proposed an algorithm for recognizing a rotator cuff supraspinatus tendon tear using a texture analysis based on a histogram, gray level co-occurrence matrix (GLCM), and gray level run length matrix (GLRLM). METHODS First, we applied a total of 57 features (5 first order descriptors, 40 GLCM features, and 12 GLRLM features) to each rotator cuff region of interest....

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 ...

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...

Abstract— Dental radiographs are essential in diagnosing the pathology of the jaw. However, similar radiographic appearance of jaw lesions causes difficulties in differentiating cyst from tumor. Therefore, we conducted a development of computer-aided classification system for cyst and tumor lesions in dental panoramic images. The proposed system consists of feature extraction based on texture u...

Early detection of medical renal disease is important as the same may lead to chronic kidney disease which is an irreversible stage. The present work proposes an efficient decision support system for detection of medical renal disease using small feature space consisting of only second order GLCM statistical features computed from raw renal ultrasound images. The GLCM mean feature vector and GL...

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....