Brain Tumor Texture Analysis – Using Wavelets and Fractals

Tuhin Utsab Paul; Aninda Ghosh; Samir Kumar Bandhyopadhyay

doi:doi:10.11648/j.ijmi.20160404.11

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Brain Tumor Texture Analysis – Using Wavelets and Fractals

Tuhin Utsab Paul, Aninda Ghosh, Samir Kumar Bandhyopadhyay

Published in International Journal of Medical Imaging (Volume 4, Issue 4)

Received: 12 July 2016 Accepted: 26 July 2016 Published: 15 August 2016

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Abstract

Brain tumor segmentation is quite popular area of research but detection of its surface texture is challenging for researchers. Normally, MRI datasets have very low resolution. This paper utilizes image enhancement technique based on wavelet. It is used to scale the low resolution image to a suitable resolution without loss. Secondly the proposed method is focused on implementation of a trained classifier using features: fractal dimension, fractal area, and wavelet average to classify type of texture present in brain tumor.

Published in	International Journal of Medical Imaging (Volume 4, Issue 4)
DOI	10.11648/j.ijmi.20160404.11
Page(s)	23-31
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Wavelet Transform, Fractal Analysis, Image Classification, Feature Extraction, Texture Analysis, Image Processing

References

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[2]	Mind the Gap: Another look at the problem of the semantic gap in image retrieval Jonathon S. Harea, Paul H. Lewisa, Peter G. B. Enserb and Christine J. Sandomb School of Electronics and Computer Science, University of Southampton, UK; School of Computing, Mathematical and Information Sciences, University of Brighton, UK.
[3]	An Efficient Algorithm for Fractal Analysis of Textures, Alceu Ferraz Costa, Gabriel Humpire-Mamani, Agma Juci Machado Traina, Department of Computer Science, University of São Paulo, USP, São Carlos, Brazil{alceufc, ghumpire, agma}.
[4]	Filterbanks and transforms Sources: Zölzer, "Digital audio signal processing," Wiley & Sons. Saramäki, "Multirate signal processing," TUT course.
[5]	Multiscale Wiener filter for the restoration of fractal signals: wavelet filter bank approach, Bor Sen Chen, Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu, Taiwan, Chin-Wei Lin.
[6]	B. Mandelbrot, "Some noises with 1/f spectrum", IEEE Trans. Inform. Theory, vol. IT-13, pp. 289-298, 1967.
[7]	A Computer Algorithm for Determining the Hausdorff Dimension of Certain Fractals By Lucy Garnett, mathematics of computation volume 51, number 183 july 1988, pages 291-300.
[8]	B. MANDELBROT, The Fractal Geometry of Nature, Freeman, San Francisco, Calif., 1983.
[9]	Fast fractal stack: fractal analysis of computed tomography scans of the lung. Alceu Ferraz Costa, Joe Tekli and Agma Juci Machado Traina, International ACM Workshop on Medical Multimedia Analysis and Retrieval (MMAR), pp. 13-18, 2011.
[10]	A k-nearest neighbor based algorithm for multi-label classification, Min-Ling Zhang, National Lab. for Novel Software Technol., Nanjing Univ., China Zhi-Hua Zhou
[11]	D. W. Aha, "Special Al review issue on lazy learning", Artificial Intelligence Review, vol. 11, 1997.
[12]	A Fast Algorithm for Multilevel Thresholding, PING-SUNG LIAO, Department of Electrical Engineering chengshiu Institute of Technology Kaohsiung, 833 Taiwan TSE-SHENG CHEN, Department of Engineering Science National Cheng Kung University Taiwan 701 Taiwan, AND PAU-CHOO CHUNG, Department of Electrical Engineering National Cheng Kung University Tainan, 701 Taiwan.
[13]	S. U. Lee and S. Y. Chung, “A comparative performance study of several global thresholding techniques for segmentation,” Computer Vision Graphics Image Processing, Vol. 52, 1990, pp. 171-190.

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APA Style

Tuhin Utsab Paul, Aninda Ghosh, Samir Kumar Bandhyopadhyay. (2016). Brain Tumor Texture Analysis – Using Wavelets and Fractals. International Journal of Medical Imaging, 4(4), 23-31. https://doi.org/10.11648/j.ijmi.20160404.11

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ACS Style

Tuhin Utsab Paul; Aninda Ghosh; Samir Kumar Bandhyopadhyay. Brain Tumor Texture Analysis – Using Wavelets and Fractals. Int. J. Med. Imaging 2016, 4(4), 23-31. doi: 10.11648/j.ijmi.20160404.11

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AMA Style

Tuhin Utsab Paul, Aninda Ghosh, Samir Kumar Bandhyopadhyay. Brain Tumor Texture Analysis – Using Wavelets and Fractals. Int J Med Imaging. 2016;4(4):23-31. doi: 10.11648/j.ijmi.20160404.11

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@article{10.11648/j.ijmi.20160404.11,
  author = {Tuhin Utsab Paul and Aninda Ghosh and Samir Kumar Bandhyopadhyay},
  title = {Brain Tumor Texture Analysis – Using Wavelets and Fractals},
  journal = {International Journal of Medical Imaging},
  volume = {4},
  number = {4},
  pages = {23-31},
  doi = {10.11648/j.ijmi.20160404.11},
  url = {https://doi.org/10.11648/j.ijmi.20160404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20160404.11},
  abstract = {Brain tumor segmentation is quite popular area of research but detection of its surface texture is challenging for researchers. Normally, MRI datasets have very low resolution. This paper utilizes image enhancement technique based on wavelet. It is used to scale the low resolution image to a suitable resolution without loss. Secondly the proposed method is focused on implementation of a trained classifier using features: fractal dimension, fractal area, and wavelet average to classify type of texture present in brain tumor.},
 year = {2016}
}

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TY  - JOUR
T1  - Brain Tumor Texture Analysis – Using Wavelets and Fractals
AU  - Tuhin Utsab Paul
AU  - Aninda Ghosh
AU  - Samir Kumar Bandhyopadhyay
Y1  - 2016/08/15
PY  - 2016
N1  - https://doi.org/10.11648/j.ijmi.20160404.11
DO  - 10.11648/j.ijmi.20160404.11
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 23
EP  - 31
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20160404.11
AB  - Brain tumor segmentation is quite popular area of research but detection of its surface texture is challenging for researchers. Normally, MRI datasets have very low resolution. This paper utilizes image enhancement technique based on wavelet. It is used to scale the low resolution image to a suitable resolution without loss. Secondly the proposed method is focused on implementation of a trained classifier using features: fractal dimension, fractal area, and wavelet average to classify type of texture present in brain tumor.
VL  - 4
IS  - 4
ER  -

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Author Information

Tuhin Utsab Paul

Department of Electronics and Communication, Institute of Engineering and Management, Kolkata, India
Aninda Ghosh

Department of Electronics and Communication, Institute of Engineering and Management, Kolkata, India
Samir Kumar Bandhyopadhyay

Department of Computer Science and Engineering, University of Calcutta, Kolkata, India

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APA Style

Tuhin Utsab Paul, Aninda Ghosh, Samir Kumar Bandhyopadhyay. (2016). Brain Tumor Texture Analysis – Using Wavelets and Fractals. International Journal of Medical Imaging, 4(4), 23-31. https://doi.org/10.11648/j.ijmi.20160404.11

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ACS Style

Tuhin Utsab Paul; Aninda Ghosh; Samir Kumar Bandhyopadhyay. Brain Tumor Texture Analysis – Using Wavelets and Fractals. Int. J. Med. Imaging 2016, 4(4), 23-31. doi: 10.11648/j.ijmi.20160404.11

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AMA Style

Tuhin Utsab Paul, Aninda Ghosh, Samir Kumar Bandhyopadhyay. Brain Tumor Texture Analysis – Using Wavelets and Fractals. Int J Med Imaging. 2016;4(4):23-31. doi: 10.11648/j.ijmi.20160404.11

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@article{10.11648/j.ijmi.20160404.11,
  author = {Tuhin Utsab Paul and Aninda Ghosh and Samir Kumar Bandhyopadhyay},
  title = {Brain Tumor Texture Analysis – Using Wavelets and Fractals},
  journal = {International Journal of Medical Imaging},
  volume = {4},
  number = {4},
  pages = {23-31},
  doi = {10.11648/j.ijmi.20160404.11},
  url = {https://doi.org/10.11648/j.ijmi.20160404.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmi.20160404.11},
  abstract = {Brain tumor segmentation is quite popular area of research but detection of its surface texture is challenging for researchers. Normally, MRI datasets have very low resolution. This paper utilizes image enhancement technique based on wavelet. It is used to scale the low resolution image to a suitable resolution without loss. Secondly the proposed method is focused on implementation of a trained classifier using features: fractal dimension, fractal area, and wavelet average to classify type of texture present in brain tumor.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Brain Tumor Texture Analysis – Using Wavelets and Fractals
AU  - Tuhin Utsab Paul
AU  - Aninda Ghosh
AU  - Samir Kumar Bandhyopadhyay
Y1  - 2016/08/15
PY  - 2016
N1  - https://doi.org/10.11648/j.ijmi.20160404.11
DO  - 10.11648/j.ijmi.20160404.11
T2  - International Journal of Medical Imaging
JF  - International Journal of Medical Imaging
JO  - International Journal of Medical Imaging
SP  - 23
EP  - 31
PB  - Science Publishing Group
SN  - 2330-832X
UR  - https://doi.org/10.11648/j.ijmi.20160404.11
AB  - Brain tumor segmentation is quite popular area of research but detection of its surface texture is challenging for researchers. Normally, MRI datasets have very low resolution. This paper utilizes image enhancement technique based on wavelet. It is used to scale the low resolution image to a suitable resolution without loss. Secondly the proposed method is focused on implementation of a trained classifier using features: fractal dimension, fractal area, and wavelet average to classify type of texture present in brain tumor.
VL  - 4
IS  - 4
ER  -

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