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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.
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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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Citations
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Development of a radiomics nomogram based on the 2D and 3D CT features to predict the survival of non-small cell lung cancer patients.

TL;DR: Both 2D and 3D radiomics signature have favorable prognosis, but 3D signature had a better performance, which further improved the predicted accuracy of survival prognosis for the patients with NSCLC.
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Malignancy characterization of hepatocellular carcinomas based on texture analysis of contrast‐enhanced MR images

TL;DR: To investigate the performance of texture analysis in characterizing malignancy of hepatocellular carcinomas (HCCs) based on contrast‐enhanced magnetic resonance imaging (MRI).
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Improving classification performance of breast lesions on ultrasonography

TL;DR: Different morphological and texture features widely used in computer-aided diagnosis systems for BUS images are compiled and evaluated for classifying breast lesions on ultrasound, revealing the best classification performance is obtained by a morphological set with five features.
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Robustness of Radiomic Features in [(11)C]Choline and [(18)F]FDG PET/CT Imaging of Nasopharyngeal Carcinoma: Impact of Segmentation and Discretization.

TL;DR: The robustness of the features was evaluated via the intraclass correlation coefficient (ICC) for seven kinds of segmentation methods and five kinds of discretization bin size and found that features extracted from [11C]choline were more robust than [18F]FDG for segmentation.
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Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

TL;DR: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast and provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment.
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.
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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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