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
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Alex Zwanenburg,Alex Zwanenburg,Martin Vallières,Mahmoud A. Abdalah,Hugo J.W.L. Aerts,Hugo J.W.L. Aerts,Vincent Andrearczyk,Aditya Apte,Saeed Ashrafinia,Spyridon Bakas,Roelof J. Beukinga,Ronald Boellaard,Marta Bogowicz,Luca Boldrini,Irène Buvat,Gary Cook,Christos Davatzikos,Adrien Depeursinge,Marie-Charlotte Desseroit,Nicola Dinapoli,Cuong V. Dinh,Sebastian Echegaray,Issam El Naqa,Issam El Naqa,Andriy Fedorov,Roberto Gatta,Robert J. Gillies,Vicky Goh,Michael Götz,Matthias Guckenberger,Sung Min Ha,Mathieu Hatt,Fabian Isensee,Philippe Lambin,Stefan Leger,Stefan Leger,Ralph T.H. Leijenaar,Jacopo Lenkowicz,Fiona Lippert,Are Losnegård,Klaus H. Maier-Hein,Olivier Morin,Henning Müller,Sandy Napel,Christophe Nioche,Fanny Orlhac,Sarthak Pati,Elisabeth Pfaehler,Arman Rahmim,Arman Rahmim,Arvind Rao,Jonas Scherer,Muhammad Siddique,Nanna M. Sijtsema,Jairo Socarras Fernandez,Emiliano Spezi,Roel J H M Steenbakkers,Stephanie Tanadini-Lang,Daniela Thorwarth,Esther G.C. Troost,Esther G.C. Troost,Taman Upadhaya,Vincenzo Valentini,Lisanne V. van Dijk,Joost J. M. van Griethuysen,Floris H. P. van Velden,Philip Whybra,Christian Richter,Christian Richter,Steffen Löck,Steffen Löck +70 more
TL;DR: A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software and could be excellently reproduced.
Journal ArticleDOI
A Comparative Study of Texture Measures for Terrain Classification
TL;DR: In this paper, three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively.
Journal ArticleDOI
Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices
Leen-Kiat Soh,Costas Tsatsoulis +1 more
TL;DR: The authors used gray-level co-occurrence matrices (GLCM) to quantitatively evaluate textural parameters and representations and to determine which parameter values and representations are best for mapping sea ice texture.
Journal ArticleDOI
Textural features corresponding to textural properties
M. Amadasun,R.A. King +1 more
TL;DR: In comparison with human perceptual measurements, the computational measures have shown good correspondences in the rank ordering of ten natural textures, and the extent to which the measures approximate visual perception was investigated in the form of texture similarity measurements, which were encouraging.
Journal ArticleDOI
A Theoretical Comparison of Texture Algorithms
TL;DR: The results obtained indicate that the SGLDM is the most powerful algorithm of the four considered, and that the GLDM is more powerful than the PSM.
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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