The current paper presents a novel texture-based method for detecting texts in images. A support vector machine (SVM) is used to analyze the textural properties of texts. No external texture feature extraction module is used; rather, the intensities of the raw pixels that make up the textural pattern are fed directly to the SVM, which works well even in high-dimensional spaces. Next, text regio...

The constant increase in the amount and resolution of remotely sensed imagery necessitates development of intelligent systems for automatic processing and classification. We describe a Bayesian framework that uses spatial information for classification of high-resolution images. First, spectral and textural features are extracted for each pixel. Then, these features are quantized and are used t...

This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradien...

The aim of the present study was to design, implement, and evaluate a software system for discriminating between metastases, meningiomas, and gliomas on MRI. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a second degree least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 75 T...

Lung cancer is the second most prevalent type of cancer. Early detection and classification of lung tumors helps in better survival. The paper presents a computer aided diagnosis (CAD) system for chest computed tomography (CT) images that focus on a new algorithm to classify lung tumor into benign or malignant using advanced neural network based classifier. Statistical textural features are ext...

We propose a framework for investigating the properties of apparently normal tissues on brain structural magnetic resonance images of patients with small vessel disease (SVD). It involves the extraction of textural features in regions of interest (ROIs) obtained from an anatomically-relevant template, combined with a statistical analysis that considers the relative distribution of SVD markers (...

Automatic mineral identification using evolutionary computation technology is discussed. Thin sections of mineral samples are digitally photographed using a computercontrolled rotating polarizer stage on a petrographic microscope. A suite of image processing functions is applied to the images. Filtered image data for identified mineral grains is then selected for use as training data for a gene...

A feature based retrieval scheme for microscopic images of diatoms in an image database is presented in this report. Diatoms are unicellular algae found in water and other places wherever there is humidity and enough light for photo synthesis. Several methods for feature extraction are described and experimental results on real diatom images are presented. The proposed feature based retrieval s...

In this paper, a computer-aided diagnostic (CAD) system for the diagnosis of benign and malignant liver tumors from computed tomography (CT) images is presented. Liver is segmented from abdominal CT images using adaptive threshold and morphological processing. Each suspicious tumor region is automatically extracted from the segmented liver using FCM technique and the textural information obtain...

Different methods of feature selection are used to improve the performance of remote sensing images classification. In this work two methods of feature selection are examined. The first one is based on the discriminant analysis, and the second one rests on building the regression model. Histogram and textural features are considered as characteristics of an image. The experiments on the remote ...