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 31, 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.
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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, Issue.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.
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|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|
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