The purpose of this work was to apply and test Haralick’s gray level co-occurrence matrix (GLCM) technique for automatic calculation and segmentation of the ischemic stroke volume from CT images. For this task, the 3nearest neighbors classifier was trained to perform stroke and non-stroke area classification. The segmentation and classification results were compared versus a manual segmentation...

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....

Oral Cancer is the most common cancer found in both men and women. The proposed system segments and classifies oral cancers at an earlier stage. The tumor is detected using Marker Controlled Watershed segmentation. The features extracted using Gray Level Co occurrence Matrix (GLCM) is Energy, Contrast, Entropy, Correlation, Homogeneity. The extracted features are fed into Support Vector Machine...

Texture is an important property used in classifying the regions of interests in an image. Literally, it is defined as the uniformity of a substance or a surface. Technically, it gives us the information about the spatial arrangement of structures in an image. One of the earliest methods used for texture feature extraction is the Gray-Level Co-occurrence Matrix (GLCM) which contains second orde...

This paper has a further exploration and study of visual feature extraction. According to the HSV (Hue, Saturation, Value) color space, the work of color feature extraction is fnished, the process is as follows: quantifying the color space in non-equal intervals, constructing one dimension feature vector and representing the color feature by cumulative histogram. Similarly, the work of texture ...

Introduction Texture analysis using 2D-image-based gray level co-occurrence matrix method [1] has been demonstrated to be useful in distinguishing between malignant and benign breast lesions in contrast-enhanced MR images [2]. 2D texture analysis does not take advantage of the 3D data in breast MR images and requires high signal-to-noise ratio, which may not be available in dynamic studies. We ...

This article presents a hybrid approach for texture-based image classification using the gray-level co-occurrence matrices (GLCM) and a new Fuzzy Kohonen Clustering Network for Symbolic Interval Data (IFKCN). The GLCM matrices extracted from an image database are processed to create the training data set using IFKCN algorithm. The IFKCN organizes and extracts prototypes from processed GLCM matr...

Print mottle is a print defect. This defect has great attention in quality assessment. determined by the grey level co-occurrence matrix (GLCM). An important parameter GLCM processing direction angle of pixels digitalized image. research aimed to investigate influence angle, which an input processing, on output parameters, such as entropy, energy, contrast, correlation, and homogeneity. Hence, ...

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 ...

Microscopic images of leaves, collected from Mona Island dry forest (which is located between Puerto Rico and the Dominican Republic), were analyzed. For each leaf side an image was obtained at two magnifications (200x and 400x). This resulted in four samples of images showing a wide variety of textures and stomata patterns. For each group of images we used the gray-level co-occurrence method t...