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CAD for Hepatic Tumor Detection in CT Images
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 163 - Number 10 |
| Year of Publication: 2017 |
| Authors: Hetvi Pasad, Himani Shetty, Ayushi Malde, Poonam Bhogale |
10.5120/ijca2017913692
|
Hetvi Pasad, Himani Shetty, Ayushi Malde, Poonam Bhogale . CAD for Hepatic Tumor Detection in CT Images. International Journal of Computer Applications. 163, 10 ( Apr 2017), 14-18. DOI=10.5120/ijca2017913692
Abstract
In the abdominal CT scan, the liver region is not clearly discerned from the adjacent organs such as muscle, spleen, and pancreas. The objective of the proposed system is to devise a novel method for tumor identification which helps the medical experts for further diagnosis. The region of interest, namely the liver, is first separated by combining ROIpoly and thresholding methods. On obtaining the liver region, the tumor if present, is extracted using Gray Level Co-occurrence Matrix (GLCM) and Fuzzy C Means (FCM). Further, we have also compared the results obtained from both the methods.
References
- http://www.mayoclinic.org/tests-procedures/ct-scan/basics/definition/prc-20014610
- Mahesh Yambal, Hitesh Gupta, “Image segmentation using Fuzzy C Means Clustering,” IJARCCE, vol.2, issue 7, pp. July 2013.
- http://in.mathworks.com/help/images/create-a-gray-level-co-occurrence-matrix.html
- https://en.wikipedia.org/wiki/Mean_squared_error
- http://www.statisticshowto.com/jaccard-index/
- https://en.wikipedia.org/wiki/Jaccard_index
- https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient
- Lauren K Haaitsma, ”Liver tumor segmentation in CT Images”, published in Data archiving and Network Services(DANS).
- Ria Benny, Dr. Tessamma Thomas, ”Automatic Detection and Classification of CT-Scan Images”, presented at 2015Fifth International Conference on Advance in Computing and Communications, IEEE.
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