Medical image texture segmentation using multifractal analysis

Korchiyne, Redouan; Sbihi, Abderrahmane; Farssi, Sidi Mohamed; Touahni, Raja; Alaoui, Mustapha Tahiri

doi:10.1109/icmcs.2012.6320316

Cited by 9 publications

(2 citation statements)

References 6 publications

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“…Fractal geometry has experienced significant growth in recent years, a review of this theory is summarised by R. Lopes [23]. In medical images analysis that the result of this theory has provided an evidence solutions to several problems [24] source of many applications [18] [25].…”

Section: Fractal Theorymentioning

confidence: 99%

A Combined Method of Fractal and GLCM Features for MRI and CT Scan Images Classification

Korchiyne¹,

Farssi²,

Sbihi³

et al. 2014

SIPIJ

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Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. The fractal feature is a compact descriptor used to give a numerical measure of the degree of irregularity of the medical images. This descriptor property does not give ownership of the local image structure. In this paper, we present a combination of this parameter based on Box Counting with GLCM Features. This powerful combination has proved good results especially in classification of medical texture from MRI and CT Scan images of trabecular bone. This method has the potential to improve clinical diagnostics tests for osteoporosis pathologies.

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Section: Fractal Theorymentioning

confidence: 99%

A Combined Method of Fractal and GLCM Features for MRI and CT Scan Images Classification

Korchiyne¹,

Farssi²,

Sbihi³

et al. 2014

SIPIJ

Self Cite

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“…Recently, multifractal analysis has received quite a bit of interest due to its many applications in different fields adjacent to mathematics. Multifractals have proved to be the pump journal of undergraduate research 3 (2020), 226-243 a useful tool in understanding turbulence in fluids [3], complex networks [10], economics [5] and medical imaging [6] just to name a few examples. Mathematically the problem is also interesting since in applications the definitions are quite non-standard and often establishing the multifractal formalism in a mathematically rigorous way requires careful investigation of the measures in question.…”

Section: Introductionmentioning

confidence: 99%

Local Entropy and Lq-Dimensions of Measures in Doubling Metric Spaces

Anttila

2020

PUMP J. Undergrad. Res.

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We define restricted entropy and Lq-dimensions of measures in doubling metric spaces and show that these definitions are consistent with the monotonicity of Lq-dimensions. This provides a correct proof for a theorem considering the relationships between local entropy and Lq-dimensions in a paper by Käenmäki, Rajala and Suomala, the original proof of which makes use of a slightly erroneous proposition.

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Partial Fractional Derivative (PFD) based Texture Analysis Model for Medical Image Segmentation

Hemalatha

Anouncia

2018

Knowledge Computing and Its Applications

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Medical image texture segmentation using multifractal analysis

Cited by 9 publications

References 6 publications

A Combined Method of Fractal and GLCM Features for MRI and CT Scan Images Classification

A Combined Method of Fractal and GLCM Features for MRI and CT Scan Images Classification

Local Entropy and Lq-Dimensions of Measures in Doubling Metric Spaces

Partial Fractional Derivative (PFD) based Texture Analysis Model for Medical Image Segmentation

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