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Texture analysis using gray level run lengths

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TLDR
In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.
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This article is published in Computer Graphics and Image Processing.The article was published on 1975-06-01. It has received 1848 citations till now. The article focuses on the topics: Image texture & Texture (geology).

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Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

TL;DR: Radiomics with machine learning using 18F-fluorodeoxyglucose (FDG)-PET in patients with pancreatic cancer provided useful prognostic information.
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Ultrasound characterization of acute myocardial ischemia by quantitative texture analysis

TL;DR: It is concluded that quantitative echocardiographic texture analysis using polar format data can identify subtle changes in myocardial texture such as that due to acute ischemia, using data acquired through the chest wall.

Radar remote sensing data for applications in forestry.

D.H. Hoekman
TL;DR: In this paper, an investigation into the applications of radar remote sensing in forestry is described, including the use of spatial analysis techniques and the potential applications for radar data for classification and biophysical parameter estimation.
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3D Solid Texture Classification Using Locally-Oriented Wavelet Transforms

TL;DR: Three novel local alignment criteria for higher-order 3D Riesz–wavelet transforms are proposed and compared and the importance of local directional information for robust and accurate solid texture recognition is demonstrated.
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Liver fibrosis staging using CT image texture analysis and soft computing

TL;DR: A non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images and showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process.
References
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Journal ArticleDOI

Textural Features for Image Classification

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI

Gray-Level Manipulation Experiments for Texture Analysis

TL;DR: Some gray-level manipulation techniques are described, the first of which involves changing thegray-level distribution within the picture, and a method for extracting relatively noise-free objects from a noisy background is described.
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