Journal ArticleDOI
An efficient differential box-counting approach to compute fractal dimension of image
N. Sarkar,Bidyut B. Chaudhuri +1 more
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TLDR
An efficient differential box-counting approach to estimate fractal dimension is proposed and by comparison with four other methods, it has been shown that the method is both efficient and accurate.Abstract:
Fractal dimension is an interesting feature proposed to characterize roughness and self-similarity in a picture. This feature has been used in texture segmentation and classification, shape analysis and other problems. An efficient differential box-counting approach to estimate fractal dimension is proposed in this note. By comparison with four other methods, it has been shown that the authors, method is both efficient and accurate. Practical results on artificial and natural textured images are presented. >read more
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Book
Algorithms for image processing and computer vision
TL;DR: Algorithms for Image Processing and Computer Vision, 2nd Edition provides the tools to speed development of image processing applications.
Texture Analysis Methods - A Review
TL;DR: Methods for digital-image texture analysis are reviewed based on available literature and research work either carried out or supervised by the authors.
Journal ArticleDOI
An improved box-counting method for image fractal dimension estimation
Jian Li,Qian Du,Caixin Sun +2 more
TL;DR: A new model is proposed to assign the smallest number of boxes to cover the entire image surface at each selected scale as required, thereby yielding more accurate estimates of fractional dimension estimation accuracy.
Journal ArticleDOI
Quantifying tumour heterogeneity with CT.
Balaji Ganeshan,Kenneth A. Miles +1 more
TL;DR: Emerging studies show that CT texture analysis has the potential to be a useful adjunct in clinical oncologic imaging, providing important information about tumour characterization, prognosis and treatment prediction and response.
Journal ArticleDOI
A Feature Set for Cytometry on Digitized Microscopic Images
TL;DR: The feature sets that have been developed and used for quantitative cytology at the Laboratory for Biomedical Image Analysis of the GSF as well as at the Center for Image Analysis in Uppsala over the last 25 years are described and illustrated.
References
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Book
The Fractal Geometry of Nature
TL;DR: This book is a blend of erudition, popularization, and exposition, and the illustrations include many superb examples of computer graphics that are works of art in their own right.
Journal ArticleDOI
Fractal-Based Description of Natural Scenes
TL;DR: The3-D fractal model provides a characterization of 3-D surfaces and their images for which the appropriateness of the model is verifiable and this characterization is stable over transformations of scale and linear transforms of intensity.
Journal ArticleDOI
Multiple Resolution Texture Analysis and Classification
TL;DR: Textures are classified based on the change in their properties with changing resolution, and the relation of a texture picture to its negative, and directional properties are discussed.
Journal ArticleDOI
Texture description and segmentation through fractal geometry
TL;DR: A new method for estimating the fractal dimension from image surfaces is presented and it is shown that it performs better at describing and segmenting generated fractal sets.