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 use of textural analysis to locate features in geophysical data
Gordon R. J. Cooper,Duncan Cowan +1 more
TL;DR: New texture filters (based on grey-level co-occurrence matrices (GLCMs) have been specifically designed for gravity and magnetic data, and are useful for the detection of subtle monopolar and dipolar geophysical anomalies.
Journal ArticleDOI
Quantitative bone imaging biomarkers to diagnose temporomandibular joint osteoarthritis.
Jonas Bianchi,Jonas Bianchi,João Roberto Gonçalves,A.C. de Oliveira Ruellas,Lawrence M Ashman,Jean-Baptiste Vimort,Marilia Yatabe,Beatriz Paniagua,Pablo Hernandez,Erika Benavides,Fabiana N. Soki,Marcos Ioshida,Lucia H. S. Cevidanes +12 more
TL;DR: 13 imaging bone biomarkers presented an acceptable diagnostic performance for the diagnosis of TMJ OA, indicating that the texture and geometry of the subchondral bone microarchitecture may be useful for quantitative grading of the disease.
Journal ArticleDOI
Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review
Gerard Walls,S.O.S. Osman,K.H. Brown,Karl T. Butterworth,Gerard G Hanna,Alan R. Hounsell,Conor K. McGarry,R.T.H. Leijenaar,Philippe Lambin,Aidan J Cole,Suneil Jain +10 more
TL;DR: The Radiomics Quality Score was applied to the full text of included papers as mentioned in this paper, and the median radiomics quality score was 11% (0-47). No studies externally validated a radiomics signature in a registered prospective study.
Journal ArticleDOI
Prostate Disease Diagnosis from CT Images Using GA Optimized SMRT Based Texture Features
TL;DR: An attempt is made to identify the types of prostate diseases from abdomen CT images of the patients using texture analysis using Sequency based Mapped Real Transform.
Journal ArticleDOI
Lung Segmentation and Characterization in COVID-19 Patients for Assessing Pulmonary Thromboembolism: An Approach Based on Deep Learning and Radiomics
Vitoantonio Bevilacqua,Nicola Altini,Berardino Prencipe,Antonio Brunetti,Laura Villani,Antonello Sacco,Chiara Morelli,Michele Ciaccia,Arnaldo Scardapane +8 more
TL;DR: In this paper, a deep learning-based segmentation model that utilizes convolutional neural networks (CNNs) in order to accurately segment the lung and lesions was developed. But the results obtained from the segmentation task are promising, allowing to reach a Dice coefficient higher than 97%, posing the basis for analysis concerning the assessment of pulmonary thromboembolism onset.
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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