This paper presents a preliminary study for mapping sea ice patterns (texture) with 100-m ERS-1 synthetic aperture radar (SAR) imagery. We used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture. We conducted experiments on the quantization le...

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....

Faculty of Electrical Engineering, Mathematics and Computer Science CE-MS-2009-25 Feature extraction is a key function in various image processing applications. A feature is an image characteristic that can capture certain visual property of the image. Texture is an important feature of many image types, which is the pattern of information or arrangement of the structure found in a picture. Tex...

This study proposes a novel method for multichannel image gray level co-occurrence matrix (GLCM) texture representation. It is well known that the standard procedure for the automatic extraction of GLCM textures is based on a mono-spectral image. In real applications, however, the GLCM texture feature extraction always refers to multi/hyperspectral images. The widely used strategy to deal with ...

Grey level co-occurrence matrix analysis (GLCM) is a well-known mathematical method for quantification of cell and tissue textural properties, such as homogeneity, complexity and level of disorder. Recently, it was demonstrated that this method is capable of evaluating fine structural changes in nuclear structure that otherwise are undetectable during standard microscopy analysis. In this artic...

OBJECTIVES To analyze the texture features on cranial sonography in preterm neonates with white matter injury quantitatively and to correlate these features with magnetic resonance imaging (MRI). METHODS The study included 33 preterm neonates treated in our neonatal intensive care unit who underwent serial cranial sonography and brain MRI near term. Patients were subdivided into 3 groups acco...

Automated grading system plays an important role in many industries. In ceramic industries, the grading of ceramic tiles is a difficult task as it has huge variations of surface properties. In this paper, automated surface grading system based on Discrete Wavelet Transform (DWT) and Gray Level Co-occurrence Matrices (GLCM) is presented. Texture information’s in ceramic tiles are effectively rep...

In this study we proposes an effective content based image retrieval by color and texture based on wavelet coefficient method to achieve good retrieval in efficiency. Color feature extraction is done by color Histogram. The texture feature extraction is acquired by Gray Level Coocurence Matrix (GLCM) or Color Coocurence Matrix (CCM). This study provides better result for image retrieval by inte...

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...

The browning kinetic was recorded using color data information from images of avocado slices. GLCM image texture was used to describe the reaction. In the experiment, images of avocado slices stored at 4°C were captured and saved in tiff format. The classical color intensity index (the mean L* value) and some statistics GLCM image textures were used. Results showed that it is possible to use GL...