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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Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool
Marie Manon Krebs Krarup,Lotte Nygård,Ivan R. Vogelius,Ivan R. Vogelius,Flemming L. Andersen,Gary Cook,Vicky Goh,Barbara M. Fischer,Barbara M. Fischer +8 more
TL;DR: The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology, and it is relevant to question whether RFs are stable enough to provide clinically useful information.
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Differentiation of lipoma from liposarcoma on MRI using texture and shape analysis.
Rebecca E. Thornhill,Mohammad Golfam,Adnan Sheikh,Greg O. Cron,Eric A. White,Joel Werier,Mark E. Schweitzer,Gina Di Primio +7 more
TL;DR: CAD may help radiologists distinguish lipoma from liposarcoma, and using optimum-threshold criteria, CAD produced superior values compared to radiologist A, 75, 83, and 80 and radiologist B, respectively.
Multiscale Anisotropic Texture Analysis and Classification of Photographic Prints
Patrice Abry,Stéphane Roux,Herwig Wendt,Paul Messier,Andrew G. Klein,Nicolas Tremblay,Pierre Borgnat,Stéphane Jaffard,Béatrice Vedel,Jim Coddington,Lee Ann Daffner +10 more
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Grading glioma by radiomics with feature selection based on mutual information
TL;DR: The proposed non-invasive solution for the grading of glioma can potentially hasten treatment decision, with the use of a de-redundancy algorithm that significantly improved the prediction accuracy.
Journal ArticleDOI
Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image
Aqib Ali,Salman Qadri,Wali Khan Mashwani,Wiyada Kumam,Poom Kumam,Poom Kumam,Samreen Naeem,Atila Göktaş,Farrukh Jamal,Christophe Chesneau,Sania Anam,Muhammad Sulaiman +11 more
TL;DR: This research introduces the novel clustering-based automated region growing framework and demonstrates the ability of machine learning methods for the segmentation and classification of diabetic retinopathy through two-dimensional retinal fundus images.
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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