Liver Tumor Detection by Classification through FD Enhancement of CT Image
In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
[1] Weimin Huang; Ning Li; Ziping Lin; Guang-Bin Huang; Weiwei Zong;
Jiayin Zhou; Yuping Duan, "Liver tumor detection and segmentation
using kernel-based extreme learning machine," Engineering in Medicine
and Biology Society (EMBC), 35th Annual International Conference of
the IEEE, vol., no., pp.3662, 3665, 3-7 July 2013.
[2] Zhang, Xing, Jie Tian, Dehui Xiang, Xiuli Li, and Kexin Deng.
"Interactive liver tumor segmentation from ct scans using support vector
classification with watershed." In Engineering in Medicine and Biology
Society, EMBC, 2011 Annual International Conference of the IEEE, pp.
6005-6008, 2011.
[3] Kumar, S.S.; Moni, R.S.; Rajeesh, J., "Liver tumor diagnosis by gray
level and contourlet coefficients texture analysis," Computing,
Electronics and Electrical Technologies (ICCEET), 2012 International
Conference on, vol., no., pp.557-562, 21-22 March 2012
[4] Maini, Raman, and Himanshu Aggarwal. "A comprehensive review of
image enhancement techniques." Volume 2, Issue 3, March 2010.
[5] Sabatier, J., Om P. Agrawal, and JA Tenreiro Machado. Advances in
fractional calculus. Vol. 4, no. 9. Dordrecht: Springer, 2007.
[6] Yi-fei, Pu, Wang Wei-xing, Zhou Ji-liu, Wang Yi-yang, and Jia Huading.
"Fractional-order derivative detection of texture of image and the
realize of fractional-order derivative filtering." Science in China, vol. 38,
no. 12 pp. 2252-2272, 2008.
[7] Yi-Fei Pu; Ji-Liu Zhou; Xiao Yuan, "Fractional Differential Mask: A
Fractional Differential-Based Approach for Multiscale Texture
Enhancement," Image Processing, IEEE Transactions on, vol.19, no.2,
pp.491,511, Feb. 2010
[8] Jalab, Hamid A., and Rabha W. Ibrahim. "Texture enhancement for
medical images based on fractional differential masks." Discrete
Dynamics in Nature and Society, pp. 1-10, 2013.
[9] Si, MaoXin, Ligang Fang, Fuyuan Hu, and Shaohui Si. "Texture
enhancement algorithm based on fractional differential mask of adaptive
non-integral step." In Image and Signal Processing (CISP), 2014 7th
International Congress on, pp. 179-183, 2014.
[10] Zhang, Jun, Zhihui Wei, and Liang Xiao. "A fast adaptive reweighted
residual-feedback iterative algorithm for fractional-order total variation
regularized multiplicative noise removal of partly-textured images."
Signal Processing Vol.98, pp.381-395, 2014.
[11] Chan, R. H., A. Lanza, S. Morigi, and F. Sgallari. "An adaptive strategy
for the restoration of textured images using fractional order
regularization." Numerical Mathematics: Theory, Methods &
Application, vol. 6, no. 1, 2013.
[12] Yu, Qiang, Fawang Liu, Ian Turner, Kevin Burrage, and Viktor Vegh.
"The use of a Riesz fractional differential-based approach for texture
enhancement in image processing." ANZIAM Journal, vol. 54, pp. 590-
607, 2013.
[13] Castellano, G., L. Bonilha, L. M. Li, and F. Cendes. "Texture analysis of
medical images." Clinical radiology 59, no. 12, pp. 1061-1069, 2004.
[14] James, Alex Pappachen, and Belur V. Dasarathy. "Medical image
fusion: A survey of the state of the art." Information Fusion, Vol 19, pp.
4-19, 2014
[15] J. R. Smith and S. F. Chang, “Automated binary texture feature sets for
image retrieval,” in Proceedings of the IEEE International Conference
on Acoustics, Speech, and Signal Processing (ICASSP ’96), pp. 2239–
2242, May 1996
[16] Srinivasan, G. N., and G. Shobha. "Statistical texture analysis." In
Proceedings of world academy of science, engineering and technology,
vol. 36, pp. 1264-1269. 2008.
[17] Kezia, Saka, I. Santi Prabha, and V. VijayaKumar. "A New Texture
Segmentation Approach for Medical Images." International Journal of
Scientific & Engineering Research, 4,2013.
[1] Weimin Huang; Ning Li; Ziping Lin; Guang-Bin Huang; Weiwei Zong;
Jiayin Zhou; Yuping Duan, "Liver tumor detection and segmentation
using kernel-based extreme learning machine," Engineering in Medicine
and Biology Society (EMBC), 35th Annual International Conference of
the IEEE, vol., no., pp.3662, 3665, 3-7 July 2013.
[2] Zhang, Xing, Jie Tian, Dehui Xiang, Xiuli Li, and Kexin Deng.
"Interactive liver tumor segmentation from ct scans using support vector
classification with watershed." In Engineering in Medicine and Biology
Society, EMBC, 2011 Annual International Conference of the IEEE, pp.
6005-6008, 2011.
[3] Kumar, S.S.; Moni, R.S.; Rajeesh, J., "Liver tumor diagnosis by gray
level and contourlet coefficients texture analysis," Computing,
Electronics and Electrical Technologies (ICCEET), 2012 International
Conference on, vol., no., pp.557-562, 21-22 March 2012
[4] Maini, Raman, and Himanshu Aggarwal. "A comprehensive review of
image enhancement techniques." Volume 2, Issue 3, March 2010.
[5] Sabatier, J., Om P. Agrawal, and JA Tenreiro Machado. Advances in
fractional calculus. Vol. 4, no. 9. Dordrecht: Springer, 2007.
[6] Yi-fei, Pu, Wang Wei-xing, Zhou Ji-liu, Wang Yi-yang, and Jia Huading.
"Fractional-order derivative detection of texture of image and the
realize of fractional-order derivative filtering." Science in China, vol. 38,
no. 12 pp. 2252-2272, 2008.
[7] Yi-Fei Pu; Ji-Liu Zhou; Xiao Yuan, "Fractional Differential Mask: A
Fractional Differential-Based Approach for Multiscale Texture
Enhancement," Image Processing, IEEE Transactions on, vol.19, no.2,
pp.491,511, Feb. 2010
[8] Jalab, Hamid A., and Rabha W. Ibrahim. "Texture enhancement for
medical images based on fractional differential masks." Discrete
Dynamics in Nature and Society, pp. 1-10, 2013.
[9] Si, MaoXin, Ligang Fang, Fuyuan Hu, and Shaohui Si. "Texture
enhancement algorithm based on fractional differential mask of adaptive
non-integral step." In Image and Signal Processing (CISP), 2014 7th
International Congress on, pp. 179-183, 2014.
[10] Zhang, Jun, Zhihui Wei, and Liang Xiao. "A fast adaptive reweighted
residual-feedback iterative algorithm for fractional-order total variation
regularized multiplicative noise removal of partly-textured images."
Signal Processing Vol.98, pp.381-395, 2014.
[11] Chan, R. H., A. Lanza, S. Morigi, and F. Sgallari. "An adaptive strategy
for the restoration of textured images using fractional order
regularization." Numerical Mathematics: Theory, Methods &
Application, vol. 6, no. 1, 2013.
[12] Yu, Qiang, Fawang Liu, Ian Turner, Kevin Burrage, and Viktor Vegh.
"The use of a Riesz fractional differential-based approach for texture
enhancement in image processing." ANZIAM Journal, vol. 54, pp. 590-
607, 2013.
[13] Castellano, G., L. Bonilha, L. M. Li, and F. Cendes. "Texture analysis of
medical images." Clinical radiology 59, no. 12, pp. 1061-1069, 2004.
[14] James, Alex Pappachen, and Belur V. Dasarathy. "Medical image
fusion: A survey of the state of the art." Information Fusion, Vol 19, pp.
4-19, 2014
[15] J. R. Smith and S. F. Chang, “Automated binary texture feature sets for
image retrieval,” in Proceedings of the IEEE International Conference
on Acoustics, Speech, and Signal Processing (ICASSP ’96), pp. 2239–
2242, May 1996
[16] Srinivasan, G. N., and G. Shobha. "Statistical texture analysis." In
Proceedings of world academy of science, engineering and technology,
vol. 36, pp. 1264-1269. 2008.
[17] Kezia, Saka, I. Santi Prabha, and V. VijayaKumar. "A New Texture
Segmentation Approach for Medical Images." International Journal of
Scientific & Engineering Research, 4,2013.
@article{"International Journal of Information, Control and Computer Sciences:71771", author = "N. Ghatwary and A. Ahmed and H. Jalab", title = "Liver Tumor Detection by Classification through FD Enhancement of CT Image", abstract = "In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.", keywords = "Fractional differential (FD), Computed Tomography
(CT), fusion.", volume = "9", number = "11", pages = "2362-4", }
