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
Classification of childhood medulloblastoma into WHO-defined multiple subtypes based on textural analysis.
TL;DR: A texture‐based computer‐aided categorization of childhood medulloblastoma samples is proposed, based on the architectural property and the distribution of cells, and it was revealed that the combined best‐4 feature set resulted in the highest accuracy of 91.3%.
Proceedings ArticleDOI
Contextual classification and segmentation of textured images
TL;DR: An algorithm which combines the merits of statistical classification- and estimation-theory-based approaches is proposed for textured image segmentation, which results in a coarse segmented image.
Journal ArticleDOI
Improving accuracy in the grading of renal cell carcinoma by combining the quantitative description of chromatin pattern with the quantitative determination of cell kinetic parameters.
Christine Francois,Christophe Moreno,Joel Teitelbaum,Gilbert Bigras,Isabelle Salmon,André Danguy,Gérard Brugal,Roland Van Velthoven,Robert Kiss,Christine Decaestecker +9 more
TL;DR: The results show that conventional RCC grading can be significantly improved by the quantitative analysis of Feulgen-stained nuclei, by cell kinetic parameter determination, and, more importantly, by combining the proliferation index with the mean AgNOR area parameter.
Book ChapterDOI
GLCM and GLRLM Based Texture Analysis: Application to Brain Cancer Diagnosis Using Histopathology Images
TL;DR: In this article, the contribution of gray-level co-occurrence matrix (GLCM) based Haralick features and grey-level run length matrix (GLRLM) features in analysis and classification of brain histopathology images is discussed.
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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