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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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Analysis of ultrasonographic prostate images for the detection of prostatic carcinoma: the automated urologic diagnostic expert system

TL;DR: The presented prospective value for image analysis was almost twice as high as the values normally found for prostate examination, and the prospective predictive value for cancer detection was 85.7%.
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Preliminary investigation into sources of uncertainty in quantitative imaging features.

TL;DR: The results suggest that substantial variation exists when textures are measured under different conditions, and thus the development of a texture analysis standard would be beneficial for comparing features between patients and institutions.
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Segmentation and Feature Extraction in Medical Imaging: A Systematic Review

TL;DR: Authors survey on various segmentation and feature extraction methods in medicinal images used for preprocessing in order to find the inner or outer construction of mortal body.
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Multiparametric MRI and radiomics in prostate cancer: a review

TL;DR: A comprehensive overview of prostate radiomics can be found in this article, with a special focus on its current applications as well as its future directions, and a review of the role of prostate MRIs in prostate cancer detection, staging and patient management is provided.
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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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