Textural features can be useful in differentiating between benign and malignant breast lesions on mammograms. Unlike previous computerized schemes, which relied largely on shape and margin features based on manual contours of masses, textural features can be determined from regions of interest (ROIs) without precise lesion segmentation. In this study, therefore, we investigated an ROI-based fea...

Abstract COVID-19 has caused over 6.35 million deaths and 555 confirmed cases till 11/July/2022. It a serious impact on individual health, social economic activities, other aspects. Based the gray-level co-occurrence matrix (GLCM), four-direction varying-distance GLCM (FDVD-GLCM) is presented. Afterward, five-property feature set (FPFS) extracts features from FDVD-GLCM. An extreme learning mach...

This study aims to identify the condition of corn plants based on imagery leaf using gray level co-occurrence matrix (GLCM) method and artificial neural network (ANN) backpropagation. The GLCM is used for extracting features from image corn, whereas ANN backpropagation classification plant features. was done a dataset leaves with four conditions: healthy, spot, blight, rust. Next, are extracted...

Breast cancer is one of the most common types among Iraqi women. MRI has been used in detection breast tumors for its efficient performance diagnosis process providing high accuracy. In this paper, image data from 89 patients were classified using GLCM and CNN feature extraction methods. Four models evaluated consisting GLCM, CNN, combined features based models. The statistical ANOVA selection ...

Th is work deals with detection of sub-lesions and major lesion in breast ultrasound (US) images. Most of the recent classificat ion uses normal and abnormal breast images to develop their algorithm. The majority of the current algorithms are interested in the major lesion when detecting the lesion boundary. US images, in first step were roughly preprocessed and classified. A function based on ...

A multichannel approach to texture description is proposed by approximating joint occurrences of multiple features with marginal distributions, as 1-D histograms, and combining similarity scores for 1-D histograms into an aggregate similarity score. A stepwise feature selection algorithm is used to choose the best feature combination in a particular dimension. In classification experiments with...

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...

The development of lidar system, especially incorporated with high-resolution camera components, has shown great potential for urban classification. However, how to automatically select the best features for land-use classification is challenging. Random Forests, a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern re...

204 words) We have developed a novel method to derive scale information from quasi-stationary images, which relies on a rotation-guided multi-scale analysis of features derived from Gray Level Co-occurrence Matrices (GLCM). Unlike other methods for multi-scale texture characterization, our method does not require rotation-invariant textural features, but instead uses orientation information der...