Journal ArticleDOI
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.About:
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).read more
Citations
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Journal ArticleDOI
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Chintan Parmar,Patrick Grossmann,Derek H. F. Rietveld,Michelle M. Rietbergen,Philippe Lambin,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts +6 more
TL;DR: This study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients and identified optimal machine- learning methods for radiomics-based prognostic analyses.
Journal ArticleDOI
Neighboring gray level dependence matrix for texture classification
Chengjun Sun,William G. Wee +1 more
TL;DR: A new approach, neighboring gray level dependence matrix (NGLDM), for texture classification is presented and it is shown that texture features can be easily computed and can be made insensitive to monotonic gray level transformation.
Journal ArticleDOI
Image characterizations based on joint gray level-run length distributions
TL;DR: In this study, some new joint run length-gray level distributions are proposed which offer additional insight into the image characterization problem and serve as effective features for a texturebased classification of images.
Journal ArticleDOI
Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features
TL;DR: A novel method for glaucoma detection using a combination of texture and higher order spectra (HOS) features from digital fundus images is presented and it is demonstrated that the texture and HOS features after z-score normalization and feature selection, and when combined with a random-forest classifier, performs better than the other classifiers.
Journal ArticleDOI
Deep Learning in Radiology.
Morgan P. McBee,Omer A. Awan,Andrew Colucci,Comeron W. Ghobadi,Nadja Kadom,Akash P. Kansagra,Srini Tridandapani,William F. Auffermann +7 more
TL;DR: An overview of deep learning for radiologists is presented in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool.
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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