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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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Patent

Method and apparatus for automatically detecting malignancy-associated changes

TL;DR: In this paper, a sample of cells is obtained and stained to identify the nuclear DNA material and the sample is imaged with a digital microscope, and objects of interest are identified based on the intensity of the pixels that comprise the object versus the average intensity of all pixels in the slide image.
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Texture Analysis of X-Ray Radiographs Is a More Reliable Descriptor of Bone Loss Than Mineral Content in a Rat Model of Localized Disuse Induced by the Clostridium botulinum Toxin

TL;DR: Bone mineral content was measured by dual-energy X-ray absorptiometry on the femur and tibia and the texture of the trabecular bone was analyzed by run length and fractal methods (skyscrapers and blanket).
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ABCA4-associated retinal degenerations spare structure and function of the human parapapillary retina

TL;DR: The retina between fovea and optic nerve head could serve as a convenient, accessible, and informative region for structural and functional studies to determine natural history or outcome of therapy in ABCA4-associated disease.
Patent

Image-based methods for measuring global nuclear patterns as epigenetic markers of cell differentiation

TL;DR: In this paper, the authors provide methods for determining the differentiation state of cells, including non-invasive, non-perturbing, automatable, and quantitative methods of analysis of cell colonies, individual cells, and/or cellular structures.
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Large-scale investigation of weed seed identification by machine vision

TL;DR: The results indicate that the naive Bayes classifier based on an adequately selected set of classification features has an excellent performance, competitive with that of the comparatively more sophisticated neural network approach.
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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