Publication 2018 April

Liver Abnormality Detection using Segmentation based Fractal Texture Analysis



Abstract - Anomalous growth of cells or tissues in the liver will lead to liver cancer which has a low survival rate. Usually, the symptoms of the liver cancer do not appear until the cancer is in the severe stage. Recent studies show that all liver abnormalities lead to malignant unless it is properly treated. So, proper detection of liver abnormality in its initial stage is very necessary. Even though the needle biopsy is a good method to detect the liver cancer, its invasive nature catalyzes the mortality of the diseases. Therefore the biomedical imaging technique is being used as noninvasive imaging methods to analyze the liver abnormality. This paper presents an efficient machine learning algorithm to recognize the liver abnormality using texture analysis. Segmentation based Fractal Texture Analysis (SFTA) is used here to extract the texture features from the segmented Liver and Naïve Bayes classifier is used to achieve the automated classification. This algorithm is successfully tested with Atlas Liver images having the abnormalities like Cirrhosis, fatty, Hepatocellular Carcinoma (HCC), hemangioma, steatohepatitis, and liver tumour. .



Abdominal CT scan, Segmentation, FCM(Fuzzy C means clustering), Feature extraction, SFTA(Segmentation based Fractal Texture Analysis), Circhosis, fatty, Hepatocellular Carcinoma (HCC), hemangioma , steatohepatitis , liver tumour, Naïve bayes, atlas liver model.




    • [1] Liaqat Ali, Khaled Khelil, Summrina K. Wajid, Zain U. Hussain, Moiz A. Shah, Adam Howard et al “Machine learning based Computer-Aided Diagnosis of liver tumours” Proc.2017 IEEE 16th Int’I conf. on Cognitive Informatics & Cognitive Computing IICCI’CC’17, 978-1-5386-0771-8/17/$31.00©2017 IEEE.

      [2] Daila S. Gridley and Michael J. Pecaut “Changes in the distribution and function of leukocytes after whole-body iron ion irradiation” on Journal of Radiation Research Advance Access published July 5, 2016 pp. 1–15 doi: 10.1093/jrr/rrw051.

      [3] Sangman Kim, Seungpyo Jung , Youngju Park, Jihoon Lee, Jusung Park  “Effective Liver Cancer Diagnosis Method based on Machine Learning Algorithm” Proc. of 2014 7th International Conference on BioMedical Engineering and Informatics (BMEI), 978-1-4799-5838-2/14/$31.00 ©2014 IEEE

      [4]  K. Malaa, V. Sadasivamb, S. Alagappan “Neural network based texture analysis of CT images for fatty and cirrhosis liver classification” 1568-4946/© 2015 Elsevier

      [5] Vinayadth V. Kohir, Sahebgoud H. Karaddi “Detection of  Brain  tumor using Back Propogation and probabilistic Neural Network”  Proceedings of 19th IRF International Conference, 25th January 2015

      [6] Ina Singh, Neelakshi Gupta “Segmentation of Liver using Hybrid K-means Clustering and Level Set” International Journal of Advanced Research in Computer Science and Software Engineering Volume 5, Issue 8, August 2015

      [7] Poonam Devi, Poonam Dabas “Liver Tumor Detection using Artificial Neural Networks for Medical Images” IJIRST –International Journal for Innovative Research in Science & Technology,Volume 2, Issue 03, August 2015.

      [8] R.Ilackiya et al,  “Fractal Texture based Image Classification” l, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.9, September- 2015, pg. 192-198.

      [9] Weimin Huang, Ning Li, Ziping Lin, Guang-Bin Huang, Weiwei Zong, Jiayin Zhou, Yuping Duan “Liver Tumor Detection and Segmentation using Kernel-based Extreme Learning Machine” Proc. of 35th Annual International Conference of the IEEE EMBS Osaka, Japan, 3 - 7 July, 2013

      [10] Sharifah Hafizah Sy Ahmad Ubaidillah, Roselina Sallehuddin, Noorfa Haszlinna  Mustaffa “Classification of Liver Cancer using Artificial Neural Network and Support Vector Machine” Proc. of Int. Conf. on Advances in Communication, Network, and Computing, CNC © Elsevier, 2014

      [11] Muhanunad Imran, Rathiah Hashim, Noor Elaiza Abd Khalid “Segmentation -based Fractal Texture Analysis and Color Layout Descriptor for Content Based Image Retrieval” International Conference on Intelligent Systems Design and Applications (ISDA) 2014.

      [12] Zheng Hua, Consolato Sergi, Patrick N. Nation, Pamela R. Wizzard, Ron O. Ball, Paul B. Pencharz, Justine M. Turner and Paul W. Wales “Hepatic ultrastructure in a neonatal piglet model of intestinal failure-associated liver disease (IFALD)” Proc. of Journal of Electron Microscopy 61(3): 179–186 (2012) doi: 10.1093/jmicro/dfs035.

      [13] Sonali Patil, V. R. Udupi “Preprocessing To Be Considered For MR and CT Images Containing Tumors”IOSR Journal of Electrical and Electronics Engineering (IOSRJEEE) ISSN: 2278-1676 Volume 1, Issue 4 (July-Aug. 2012), PP 54-57.

      [14]Oliveira, Dário AB, Raul Q. Feitosa, and Mauro M. Correia, "Segmentation of liver, its vessels and lesions from CT images for surgical planning," Biomedical engineering online 10, no. 1, 2011.

      [15] Bendi Venkata Ramana, Prof. M.Surendra Prasad Babu,“A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis” International Journal of Database Management Systems ( IJDMS ), Vol.3,No.2, May 2011.

      [16] Shigao Chen, Matthew W. Urban, Member, Cristina Pislaru, Randall Kinnick, Yi Zheng, Aiping Yao, and James F. Greenleaf, Fellow “Shearwave Dispersion Ultrasound Vibrometry (SDUV) for Measuring Tissue Elasticity and Viscosity” Proc. of IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 56, no. 1, January 2009.

      [17] Nazeih M. Botros, “A PC-Based Tissue Classification System Using Artificial Neural Networks” Proc. of IEEE transactions on instrumentation and measurement, vol. 41, no. 5, october 1992.

      [18] C. Chen, Daponte J. et al., "Fractal feature analysis and classification in medical imaging". IEEE Transaction on  Med. Imaging, 8, 133-142 1989.


Authors :


Regional center IHRD, Thiruvananthapuram


Regional center IHRD, Thiruvananthapuram


Copy Right 2017 -These are open access articles distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Click the link journals for the details of published journals