We performed a systematic review of several pattern analysis approaches for classifying breast lesions using dynamic, morphological, and textural features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Several machine learning approaches, namely artificial neural networks (ANN), support vector machines (SVM), linear discriminant analysis (LDA), tree-based classifiers (TC), a...

ABSTARCT: In this work, gray-level co-occurrence matrices (GLCM) have been used to quantitatively evaluate statistical textural parameters for a SAR image and to generate a filtered image to feed to ANNs for classification for land cover. Prior to performing the textural analysis, an adaptive filter was applied to reduce the effect of radar-system-generated coherent speckle to produces an image...

Understanding urban structures and processes is increasingly important as cities become the home of more of the earth’s population. This paper describes the use of a study area in Los Angeles, California to test the potential for integrative use of Interferometric Synthetic Aperature Radar (IFSAR) and Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) hyperspectral imagery for analysis o...

An image analysis system (IAS) was developed for the quantitative assessment of estrogen receptor’s (ER) positive status from breast tissue microscopy images. Twenty-four cases of breast cancer biopsies, immunohistochemically (IHC) stained for ER, were microscopically assessed by a histopathologist, following a clinical routine scoring protocol. Digitized microscopy views of the specimens were ...

Visen, N.S., Paliwal, J., Jayas, D.S. and White, N.D.G. 2004. Image analysis of bulk grain samples using neural networks. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada 46: 7.117.15. Algorithms were developed to acquire and process color images of bulk grain samples of five grain types, namely barley, oats, rye, wheat, and durum wheat. The developed algorithms were used to e...

In the previous chapters, we proposed several features for writer identification, historical manuscript dating and localization separately. In this chapter, we present a summarization of the proposed features for different applications by proposing a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on adjacent p...

In the previous chapters, we proposed several features for writer identification, historical manuscript dating and localization separately. In this chapter, we present a summarization of the proposed features for different applications by proposing a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on adjacent p...

In the previous chapters, we proposed several features for writer identification, historical manuscript dating and localization separately. In this chapter, we present a summarization of the proposed features for different applications by proposing a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on adjacent p...

Monitoring the status and future trends in biodiversity can be prohibitively expensive using groundbased surveys. Consequently, significant effort is being invested in the use of satellite remote sensing to represent aspects of the proximate mechanisms (e.g., resource availability) that can be related to biodiversity surrogates (BS) such as species community descriptors. We explored the potenti...

This study explored the potential of computed tomography (CT) textural feature analysis for the stratification of single large hepatocellular carcinomas (HCCs) > 5 cm, and the subsequent determination of patient suitability for liver resection (LR) or transcatheter arterial chemoembolization (TACE). Wavelet decomposition was performed on portal-phase CT images with three bandwidth responses (fi...