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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.
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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).

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An Empirical Approach for Avoiding False Discoveries When Applying High-Dimensional Radiomics to Small Datasets

TL;DR: A methodology to assess and reduce the impact of statistical fluctuations that may occur in small datasets, which was applied to MR images from small datasets in metastatic liver disease and primary uterine adenocarcinoma.
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Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution

TL;DR: This work analyzes mammographic image textures to classify regions of these images as benign or malignant using a Support Vector Machine (SVM) and believes that the proposed method, with some adaptations, may also be used for image texture analysis of several different lesions such as lung nodules, glaucoma and prostates.
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Improving Treatment Response Prediction for Chemoradiation Therapy of Pancreatic Cancer Using a Combination of Delta-Radiomics and the Clinical Biomarker CA19-9.

TL;DR: It is shown that delta radiomics features (DRF) from daily CT-guided chemoradiation therapy (CRT) is associated with early prediction of treatment response for pancreatic cancer and CA19-9 combination has the potential to increasing the possibility for response-based treatment adaptation.
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Illumination invariant head pose estimation using random forests classifier and binary pattern run length matrix

TL;DR: A novel approach for head pose estimation in gray-level images is presented and it is shown that the proposed algorithm is efficient and robust against illumination change.
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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