Skin cancer texture analysis of OCT images based on Haralick, fractal dimension and the complex directional field features

Skin cancer texture analysis of OCT images based on Haralick, fractal dimension, Markov random field features, and the complex directional field features

10.1117/12.2246446 ◽

2016 ◽

Cited By ~ 5

Author(s):

Dmitry S. Raupov ◽

Oleg O. Myakinin ◽

Ivan A. Bratchenko ◽

Valery P. Zakharov ◽

Alexander G. Khramov

Keyword(s):

Fractal Dimension ◽

Skin Cancer ◽

Random Field ◽

Texture Analysis ◽

Markov Random Field ◽

Markov Random ◽

Directional Field

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Fractal Dimension Based Texture Analysis of Digital Images

Procedia Engineering ◽

10.1016/j.proeng.2012.06.348 ◽

2012 ◽

Vol 38 ◽

pp. 2981-2986 ◽

Cited By ~ 23

Author(s):

P. Shanmugavadivu ◽

V. Sivakumar

Keyword(s):

Fractal Dimension ◽

Texture Analysis ◽

Digital Images

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Estimation of qth-Order Fractal Dimensions

Fractals ◽

10.1142/s0218348x97000802 ◽

1997 ◽

Vol 05 (supp01) ◽

pp. 257-269 ◽

Cited By ~ 1

Author(s):

Stefano Fioravanti ◽

Daniele D. Giusto

Keyword(s):

Image Processing ◽

Fractal Dimension ◽

Texture Analysis ◽

Digital Image Processing ◽

Digital Image ◽

State Of The Art ◽

Fractal Dimensions ◽

Sar Image ◽

Spectrum Function ◽

Novel Algorithm

The paper deals with the theory of qth-order fractal dimensions and its application to texture analysis. In particular, the state-of-the-art regarding the fractal dimension estimation for characterizing textures is presented. After, the insufficiency of the single fractal dimension is proven and the qth order fractal dimensions are introduced to overcome such drawback. The multifractality spectrum function D(q) is described, a novel algorithm for estimating such dimensions is then proposed, and its use in digital-image processing is addressed. Results on real SAR image textures are reported and discussed.

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ON TEXTURE CLASSIFICATION USING FRACTAL DIMENSION

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001499000513 ◽

1999 ◽

Vol 13 (06) ◽

pp. 929-943 ◽

Cited By ~ 11

Author(s):

YAN QIU CHEN ◽

GUOAN BI

Keyword(s):

Fractal Dimension ◽

Texture Analysis ◽

Texture Classification ◽

Texture Feature ◽

Experimental Results ◽

Image Texture ◽

Feature Sets ◽

Mathematical Properties ◽

Underlying Causes

The fractal dimension has been studied as a feature for texture analysis. It has been found that the fractal dimension is not an effective image texture measure but little is known about the reasons for the fractal dimension failing to be effective for texture analysis. This paper investigates into the underlying causes why the fractal dimension is not an effective image texture feature. Four mathematical properties have been identified which are responsible for the fractal dimension's ineffectiveness. The experimental results show that while the fractal dimension itself is hardly an effective feature for texture classification, it can considerably enhance other feature sets.

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Texture analysis using complex system models: fractal dimension, swarm systems and non-linear diffusion

10.11606/t.55.2016.tde-24112016-113253 ◽

2016 ◽

Author(s):

Bruno Brandoli Machado

Keyword(s):

Complex System ◽

Fractal Dimension ◽

Texture Analysis ◽

Linear Diffusion ◽

System Models ◽

Non Linear

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Multiscale Fractal Texture Analysis Using Morphological Techniques

Fractals ◽

10.1142/s0218348x97000693 ◽

1997 ◽

Vol 05 (supp01) ◽

pp. 125-131 ◽

Cited By ~ 1

Author(s):

Antony T. Popov

Keyword(s):

Fractal Dimension ◽

Texture Analysis ◽

Mathematical Morphology ◽

Parallel Implementation ◽

Linear Transformations ◽

Extrinsic Factors

Fractal dimension is used to analyse texture images. Since the fractal dimension remains unchanged under linear transformations, presented method is robust for dismissing effects caused by lighting and other extrinsic factors. The methods of mathematical morphology are used to calculate the fractal (Bouligand) dimension. A parallel implementation of morphological blanket covering is proposed.

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