{"title":"Liver Tumor Detection by Classification through FD Enhancement of CT Image","authors":"N. Ghatwary, A. Ahmed, H. Jalab","volume":107,"journal":"International Journal of Computer and Information Engineering","pagesStart":2355,"pagesEnd":2359,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10003283","abstract":"In this paper, an approach for the liver tumor detection\r\nin computed tomography (CT) images is represented. The detection\r\nprocess is based on classifying the features of target liver cell to\r\neither tumor or non-tumor. Fractional differential (FD) is applied for\r\nenhancement of Liver CT images, with the aim of enhancing texture\r\nand edge features. Later on, a fusion method is applied to merge\r\nbetween the various enhanced images and produce a variety of\r\nfeature improvement, which will increase the accuracy of\r\nclassification. Each image is divided into NxN non-overlapping\r\nblocks, to extract the desired features. Support vector machines\r\n(SVM) classifier is trained later on a supplied dataset different from\r\nthe tested one. Finally, the block cells are identified whether they are\r\nclassified as tumor or not. Our approach is validated on a group of\r\npatients\u2019 CT liver tumor datasets. The experiment results\r\ndemonstrated the efficiency of detection in the proposed technique.","references":"[1] Weimin Huang; Ning Li; Ziping Lin; Guang-Bin Huang; Weiwei Zong;\r\nJiayin Zhou; Yuping Duan, \"Liver tumor detection and segmentation\r\nusing kernel-based extreme learning machine,\" Engineering in Medicine\r\nand Biology Society (EMBC), 35th Annual International Conference of\r\nthe IEEE, vol., no., pp.3662, 3665, 3-7 July 2013.\r\n[2] Zhang, Xing, Jie Tian, Dehui Xiang, Xiuli Li, and Kexin Deng.\r\n\"Interactive liver tumor segmentation from ct scans using support vector\r\nclassification with watershed.\" In Engineering in Medicine and Biology\r\nSociety, EMBC, 2011 Annual International Conference of the IEEE, pp.\r\n6005-6008, 2011.\r\n[3] Kumar, S.S.; Moni, R.S.; Rajeesh, J., \"Liver tumor diagnosis by gray\r\nlevel and contourlet coefficients texture analysis,\" Computing,\r\nElectronics and Electrical Technologies (ICCEET), 2012 International\r\nConference on, vol., no., pp.557-562, 21-22 March 2012\r\n[4] Maini, Raman, and Himanshu Aggarwal. \"A comprehensive review of\r\nimage enhancement techniques.\" Volume 2, Issue 3, March 2010.\r\n[5] Sabatier, J., Om P. Agrawal, and JA Tenreiro Machado. Advances in\r\nfractional calculus. Vol. 4, no. 9. Dordrecht: Springer, 2007.\r\n[6] Yi-fei, Pu, Wang Wei-xing, Zhou Ji-liu, Wang Yi-yang, and Jia Huading.\r\n\"Fractional-order derivative detection of texture of image and the\r\nrealize of fractional-order derivative filtering.\" Science in China, vol. 38,\r\nno. 12 pp. 2252-2272, 2008.\r\n[7] Yi-Fei Pu; Ji-Liu Zhou; Xiao Yuan, \"Fractional Differential Mask: A\r\nFractional Differential-Based Approach for Multiscale Texture\r\nEnhancement,\" Image Processing, IEEE Transactions on, vol.19, no.2,\r\npp.491,511, Feb. 2010\r\n[8] Jalab, Hamid A., and Rabha W. Ibrahim. \"Texture enhancement for\r\nmedical images based on fractional differential masks.\" Discrete\r\nDynamics in Nature and Society, pp. 1-10, 2013.\r\n[9] Si, MaoXin, Ligang Fang, Fuyuan Hu, and Shaohui Si. \"Texture\r\nenhancement algorithm based on fractional differential mask of adaptive\r\nnon-integral step.\" In Image and Signal Processing (CISP), 2014 7th\r\nInternational Congress on, pp. 179-183, 2014.\r\n[10] Zhang, Jun, Zhihui Wei, and Liang Xiao. \"A fast adaptive reweighted\r\nresidual-feedback iterative algorithm for fractional-order total variation\r\nregularized multiplicative noise removal of partly-textured images.\"\r\nSignal Processing Vol.98, pp.381-395, 2014.\r\n[11] Chan, R. H., A. Lanza, S. Morigi, and F. Sgallari. \"An adaptive strategy\r\nfor the restoration of textured images using fractional order\r\nregularization.\" Numerical Mathematics: Theory, Methods &\r\nApplication, vol. 6, no. 1, 2013.\r\n[12] Yu, Qiang, Fawang Liu, Ian Turner, Kevin Burrage, and Viktor Vegh.\r\n\"The use of a Riesz fractional differential-based approach for texture\r\nenhancement in image processing.\" ANZIAM Journal, vol. 54, pp. 590-\r\n607, 2013.\r\n[13] Castellano, G., L. Bonilha, L. M. Li, and F. Cendes. \"Texture analysis of\r\nmedical images.\" Clinical radiology 59, no. 12, pp. 1061-1069, 2004.\r\n[14] James, Alex Pappachen, and Belur V. Dasarathy. \"Medical image\r\nfusion: A survey of the state of the art.\" Information Fusion, Vol 19, pp.\r\n4-19, 2014\r\n[15] J. R. Smith and S. F. Chang, \u201cAutomated binary texture feature sets for\r\nimage retrieval,\u201d in Proceedings of the IEEE International Conference\r\non Acoustics, Speech, and Signal Processing (ICASSP \u201996), pp. 2239\u2013\r\n2242, May 1996\r\n[16] Srinivasan, G. N., and G. Shobha. \"Statistical texture analysis.\" In\r\nProceedings of world academy of science, engineering and technology,\r\nvol. 36, pp. 1264-1269. 2008.\r\n[17] Kezia, Saka, I. Santi Prabha, and V. VijayaKumar. \"A New Texture\r\nSegmentation Approach for Medical Images.\" International Journal of\r\nScientific & Engineering Research, 4,2013. ","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 107, 2015"}