International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed Scientific Research Publishing Journal
Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier
Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier
R. Sreeraj1 , G. Raju2

Section:Research Paper, Product Type: Journal Paper
Volume-4 , Issue-11 , Page no. 26-29, Nov-2016

Online published on Nov 29, 2016

Copyright © R. Sreeraj, G. Raju . 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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Citation

IEEE Style Citation: R. Sreeraj, G. Raju, “Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.11, pp.26-29, 2016.

MLA Style Citation: R. Sreeraj, G. Raju "Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier." International Journal of Computer Sciences and Engineering 4.11 (2016): 26-29.

APA Style Citation: R. Sreeraj, G. Raju, (2016). Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier. International Journal of Computer Sciences and Engineering, 4(11), 26-29.
           
Abstract :
This paper presents an approach to automatic detection of liver tumor in CT images by using region-growing and Support Vector Machine (SVM) which is successfully classifies the liver cancer types such as hepatoma, hemangioma and carcinoma.The method rectifies the problem of manual segmentation and classification which is time consuming due to the variance in the characteristics of CT images.Our proposed method has been tested on a group of CT images obtained from hospitals in Kerala with a promising results both in liver and tumor segmentation. The average error rate and accuracy rate obtained from our proposed method is 0.02 and 0.9.
Key-Words / Index Term :
Region-growing,preprocessing,feature extraction,Segmentation, SVM Classifier.
References :
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