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
Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration
TL;DR: This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively and is of potential in many brain MR image processing applications that require expedition and automation.
Journal ArticleDOI
Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies.
Jake Kendrick,Roslyn J. Francis,Roslyn J. Francis,Ghulam Mubashar Hassan,Pejman Rowshanfarzad,Robert Jeraj,Robert Jeraj,Collin Kasisi,Branimir Rusanov,Martin A. Ebert,Martin A. Ebert +10 more
TL;DR: A recent review as mentioned in this paper provides an overview of the radiomics methodology, detailing critical aspects affecting the reproducibility of features, and providing examples of how artificial intelligence techniques can be incorporated into the workflow.
Proceedings ArticleDOI
Texture or Color Analysis in Agronomic Images for Wheat Ear Counting
Frédéric Cointault,Pierre Gouton +1 more
TL;DR: The use of color and texture image processing together to detect the ears is presented and different texture image segmentation techniques based on feature extraction by first and higher order statistical methods are proposed.
Proceedings ArticleDOI
Integrated color and texture tools for colposcopic image segmentation
TL;DR: An integrated analysis tool for helping gynecologists to build their colposcopic diagnosis by allowing one to choose the different color components and texture attributes which are to be taken into account in order to perform segmentation.
Proceedings ArticleDOI
How Well Do Computational Features Perceptually Rank Textures? A Comparative Evaluation
TL;DR: This paper compares texture rankings derived by 51 sets of computational features against perceptual texture rankings obtained from a free-grouping experiment with 30 human observers, using a unify evaluation framework to show which feature sets perform better than their counterparts.
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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