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
Texture description and segmentation through fractal geometry
TL;DR: A new method for estimating the fractal dimension from image surfaces is presented and it is shown that it performs better at describing and segmenting generated fractal sets.
Journal ArticleDOI
Radiomics: the facts and the challenges of image analysis
Stefania Rizzo,Francesca Botta,Sara Raimondi,Daniela Origgi,Cristiana Fanciullo,Alessio G. Morganti,Massimo Bellomi +6 more
TL;DR: The major issues regarding this multi-step process, focussing in particular on challenges of the extraction of radiomic features from data sets provided by computed tomography, positron emission tomographic, and magnetic resonance imaging are summarised.
Journal ArticleDOI
Approaches for automated detection and classification of masses in mammograms
TL;DR: The methods for mass detection and classification for breast cancer diagnosis are discussed, and their advantages and drawbacks are compared.
Journal ArticleDOI
Image texture analysis: methods and comparisons
TL;DR: An overview of several different approaches to image texture analysis is provided and insight into their space/frequency decomposition behavior is used to show why they are generally considered to be state of the art in texture analysis.
Journal ArticleDOI
Measuring Computed Tomography Scanner Variability of Radiomics Features.
Dennis Stephen Mackin,X Fave,Lifei Zhang,David Fried,Jinzhong Yang,Brian E. Taylor,Edgardo Rodriguez-Rivera,Cristina Dodge,Aaron Kyle Jones,Laurence E. Court +9 more
TL;DR: The variability in the values of radiomics features calculated on CT images from different CT scanners can be comparable to the variability in these features found in CT images of NSCLC tumors.
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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