Journal ArticleDOI
Texture analysis using gray level run lengths
Reads0
Chats0
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
More filters
Journal ArticleDOI
Radiomics: Principles and radiotherapy applications.
Isabelle Gardin,V. Grégoire,David Gibon,Hortense A. Kirisli,David Pasquier,Juliette Thariat,Pierre Vera +6 more
TL;DR: This review addresses the research supporting the clinical use of radiomics in oncology in the staging of disease, discrimination between healthy and pathological tissues, the identification of genetic features, the prediction of patient survival, the response to treatment, the recurrence after radiotherapy and chemoradiotherapy and the side effects.
Journal ArticleDOI
Lung-Nodule Classification Based on Computed Tomography Using Taxonomic Diversity Indexes and an SVM
Antonio Oseas de Carvalho Filho,Aristófanes Corrêa Silva,Anselmo Cardoso de Paiva,Rodolfo Acatauassú Nunes,Marcelo Gattass +4 more
TL;DR: The presented methodology shows promising results for classifying nodules and non-nodules, presenting a mean accuracy of 98.11% and a Support Vector Machine (SVM) as a classifier.
Journal ArticleDOI
Automatic Detection of Concrete Spalling Using Piecewise Linear Stochastic Gradient Descent Logistic Regression and Image Texture Analysis
TL;DR: The proposed PL-SGDLR can be an effective tool for maintenance agencies during periodic survey of buildings and enhance the capability of logistic regression in dealing with spall detection as a complex pattern classification problem.
Journal ArticleDOI
Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment.
TL;DR: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis.
Journal ArticleDOI
Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction.
Emmanuel G. Pintelas,Meletis Liaskos,Ioannis E. Livieris,Sotiris Kotsiantis,Panayiotis E. Pintelas +4 more
TL;DR: An accurate and interpretable machine learning framework is proposed, for image classification problems able to make high quality explanations, and achieves sufficient prediction accuracy being also interpretable and explainable in simple human terms.
References
More filters
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.
Related Papers (5)
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Hugo J.W.L. Aerts,Emmanuel Rios Velazquez,Ralph T.H. Leijenaar,Chintan Parmar,Patrick Grossmann,Sara Carvalho,Sara Cavalho,Johan Bussink,René Monshouwer,Benjamin Haibe-Kains,Derek H. F. Rietveld,Frank J. P. Hoebers,Michelle M. Rietbergen,C. René Leemans,Andre Dekker,John Quackenbush,Robert J. Gillies,Philippe Lambin +17 more