A critical shortcoming of determining co-occurrence probability texture features using Haralick’s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. In this paper, the design, implementation, and testing of a more efficient algorithm to perform this task are presented. This algorithm, known as the grey level co-occurrence integrated algorithm (GLCIA), is a dra...

Image classification is a process of classifying an image based upon the training given to a classifier. There are various purposes of classification but in this work a Brain MRI image is taken as input and is mainly classified into three class’s malignant tumor and benign tumor and non tumor by using Neural Network classifier. Here a Gaussian Mixture model is used for the purpose of segmentati...

OBJECTIVE To investigate the feasibility and accuracy of texture analysis to distinguish through objective and quantitative image information between healthy and infarcted myocardium with computed tomography (CT). MATERIALS AND METHODS Twenty patients (5 females; mean age 56±10years) with proven acute myocardial infarction (MI) and 20 patients (8 females; mean age 42±15years) with no cardiac ...

In this study we present an image analysis methodology capable of quantifying morphological changes in tissue collagen fibril organization caused by pathological conditions. Texture analysis based on first-order statistics (FOS) and second-order statistics such as gray level co-occurrence matrix (GLCM) was explored to extract second-harmonic generation (SHG) image features that are associated w...

Recently several papers have appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP plus LBP and statistical measures from gray level co-occurrence matrices (GLCM) with MCY...

Statistical Geometric Features (SGF) have recently been proposed for the classification of image textures. The SGF method is easily extended to use other geometric properties of connected regions. Following a brief review of the method, we propose such an extension to the set of SGF features for the purpose of classifying cervical cell textures. The resulting method proves to be as powerful as ...

Texture is an important spatial feature, useful for identifying objects or regions of interest in an image. One of the most popular statistical methods used to measure the textural information of images is the grey-level co-occurrence matrix (GLCM). The other statistical approach to texture analysis is the texture spectrum approach. The present paper combines the fuzzy texture unit and GLCM app...

Content-based Image Retrieval (CBIR) has gained much attention in the past decades. CBIR is a technique to retrieve images from an image database such that the retrieved images are semantically relevant to a query image provided by a user. It is based on representing images by using low-level visual features, which can be extracted from images such as color, texture and shape. Each of the featu...

A large number of spatial feature extraction methods were developed during the past 20 years. The effectiveness of each method has been assessed in different studies using different data. However, there have been few application-oriented studies made to evaluate the relative powers of these methods in a particular environment. In this study, three spatial feature extraction methods have been co...