|A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques|
|R.V. PATIL1* , S.S. SANNAKKI2 , V.S. RAJPUROHIT3|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-3 , Page no. 29-34, Mar-2017
Online published on Mar 25, 2017
Copyright © R.V. PATIL, S.S. SANNAKKI, V.S. RAJPUROHIT . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|XML View||PDF Download|
IEEE Style Citation: R.V. PATIL, S.S. SANNAKKI, V.S. RAJPUROHIT, “A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques”, International Journal of Computer Sciences and Engineering, Vol.5(3), pp.29-34, 2017.
MLA Style Citation: R.V. PATIL, S.S. SANNAKKI, V.S. RAJPUROHIT "A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques." International Journal of Computer Sciences and Engineering 5.3 (2017): 29-34.
APA Style Citation: R.V. PATIL, S.S. SANNAKKI, V.S. RAJPUROHIT, (2017). A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques. International Journal of Computer Sciences and Engineering, 5(3), 29-34.
|Human soft tissues are diagnosed by different imaging modalities such as Computed tomography CT, Ultrasound US, Magnetic resonance imaging MRI all these imaging modalities are applied depending on the nature of the disease. In the classification of liver related diseases each of these imaging modalities plays important role. Classifying a liver into normal liver and diseased liver (in diseased cirrhotic or fatty liver) depends completely on the texture of the liver. Texture is a combination of repeated patterns with regular or irregular frequency. Texture visualization is easier but very difficult to describe in words. Analyzing liver texture is also difficult. To classify liver into its respective diseases category it is very important to extract the Region of Interest ROI accurately by segmentation, but as liver structure has maximum disparity in intensity texture inside and along boundary which serves as a major problem in its segmentation and classification. There are different textural analysis techniques developed for liver classification over the years some of which work equally well for most of the imaging modalities. Here, an attempt is made to summarize the importance of textural analysis techniques devised for different imaging modalities.|
|Key-Words / Index Term :|
|Cirrhotic, fatty liver, Ultrasound, Computed tomography, Magnetic resonance imaging, Texture Analysis, Liver Classification|
 Rathore S, Iftikhar M, Hussain M, Jalil A, “Texture analysis for liver segmentation and classification: a survey”, Frontiers of Information Technology (FIT), Islamabad, pp.121–126, 2011, Print ISBN 978-1-4673-0209-8
 Mougiakakou S G, Valavanis I, Konstantina S N, Alexandra N and D. Kelekis., “Characterization of CT liver lesions based on texture features and a multiple Neural Network classification scheme”, Engineering in Medicine and Biology Society, Mexico, pp.1287 - 1290, 2003, Print ISBN 0-7803-7789-3.
 Mala K, Sadasivam V., “Automatic segmentation and classification of diffused liver diseases using wavelet based texture analysis and Neural Network”, INDICON 2005 Annual IEEE, NA, pp. 216-219, 2005, Print ISBN 0-7803-9503-4.
 Mala K, Sadasivam V., “Wavelet based texture analysis of Liver tumor from Computed Tomography images for characterization using Linear Vector Quantization Neural Network”, Advanced Computing and Communications International conference, Surathkal, pp. 267 – 270, 2006, E-ISBN: 1-4244-0716-8.
 Subbiah V. B, Vijilious M.A.Leo, and Ganesan L, “Orthogonal Moments based texture analysis of CT liver images”, International Conference on Computational Intelligence and Multimedia Applications, Tamilnadu-India, pp.249-253, 2007, Print ISBN: 0-7695-3050-8
 Nawaz S, and Dar A.H., “Hepatic lesions classification by ensemble of SVMs using statistical features based on co-occurrence matrix”, International Conference on Emerging Technologies, IEEE-ICET- 2008, Rawalpindi-Pakistan, pp.21-26, 2008, Print ISBN: 978-1-4244-2210-4
 Sreeraj R, Raju. G “Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier” IJCSE Volume-4, Issue-11, 2016
 Ahmadian A, Mostafa A, Abolhassani M.D, and Salimpour Y, “A texture classification method for diffused liver diseases using Gabor wavelets”, 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005- Shanghai- China, pp. 1567 – 1570, 2005, Print ISBN: 0-7803-8741-4.
 Balasubramanian D, Srinivasan P and Gurupatham R, “Automatic classification of focal lesions in ultrasound liver images using Principal Component Analysis and Neural Networks”, 29th Annual International Conference of the IEEE Engineering and Medicine and Biology Society- 2007, Lyon-France, pp.2134-2137, 2007, Print ISBN: 978-1-4244-0787-3.
 Hwanga Y N, Lee JH, Kim GY, Jiang YY and Kim SM “Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network”, Bio-Medical Materials and Engineering, Shanghai-China , pp. S1599–S1611, 2015.
 Neogi N, Adhikari A, Roy M “Classification of Ultrasonography Images of Human Fatty and Normal Livers using GLCM Textural Features”, Current Trends in Technology and Science, Vol 3(4), pp.252-259, 2014
 Haralik R. M, and Shanmuygam K, “Textural features for image classification”, IEEE Transactions on systems, Man, Cybernetics., Vol. SMC-3(6), pp. 610-621, 1973.
 Luo S, Qingmao H, Xiangjian H, Jiaming L, Jesse S.Jin, Mira P , “Automatic liver parenchyma segmentation from abdominal CT images using Support Vector Machines” , ICME International Conference on Complex Medical Engineering(2009), Tempe-USA, pp.1-5, 2009
 Cao G, Shi P, and Hu B, “Liver fibrosis identification based on ultrasonic images”, Engineering in Medicine and Biology Society, 2005, Shangai, pp.6317 – 6320, 2005.
 Virmani J, Kumar V, Kalra N, Khandelwal N. “SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors”, Jounal of Digit Imaging, Vol 26(3), pp.530-543, 2013
 Minhas F, Sabih D, Hussain M. “Automated classification of liver disorders using ultrasound images”, Journal of Medical Systems, Vol 36(5), pp.3163–3172, 2012.
 Andrade A, Silva JS, Santos J, Belo-Soares P. “Classifier approaches for liver steatosis using ultrasound images”, Procedia Technology, Vol 5, pp. 763-770,2012.
 Huang Y, Han X, Tian X, Zhao Z, Zhao J, Hao D. “Texture analysis of ultrasonic liver images based on spatial domain methods”, Image and Signal Processing (CISP)2010, Yantai-China, pp. 562–565, 2010