We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images

by Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 4
Year of Publication: 2017
Authors: Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa
10.5120/ijca2017915917

Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa . An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images. International Journal of Computer Applications. 179, 4 ( Dec 2017), 12-18. DOI=10.5120/ijca2017915917

@article{ 10.5120/ijca2017915917,
author = { Roaa G. Mohamed, Noha A. Seada, Salma Hamdy, Mostafa G. M. Mostafa },
title = { An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 179 },
number = { 4 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number4/28723-2017915917/ },
doi = { 10.5120/ijca2017915917 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:54:25.199027+05:30
%A Roaa G. Mohamed
%A Noha A. Seada
%A Salma Hamdy
%A Mostafa G. M. Mostafa
%T An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 4
%P 12-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Despite the importance of the liver segmentation in the medical images for efficient noninvasive diagnosis, few studies found in the literatures for fully automated methods for liver segmentation in Magnetic Resonance Imaging (MRI) compared to that in Computed Tomography (CT) scans. Motivated by this, we propose an adaptive fully automatic liver segmentation method for MRI images based on thresholding and Bayesian classification. Bayesian classifications have proved to be highly robust to various image degradations. It only requires a small amount of training data to estimate the parameters necessary for classification, which is a huge advantage in medical applications. Furthermore, the Bayesian model is robust when large uncertainties are involved in medical image analysis problems. The proposed method is successfully tested on many MRI cases acquired from different patients, in various sizes. Experiments proved the robustness of the proposed liver automatic segmentation process even on data from different scanner types. The segmentation accuracy of the model has a mean Dice Similarity Coefficient (DSC) of 95.5% for MRI datasets. General terms Image processing, Computer Vision.

References
  1. S. Kiruthika and I. K. Raj, “Liver Extraction Using Histogram and Morphology,” IJRET, pp. 245–249, 2016.
  2. I. Singh, “A Study of Effective Segmentation Techniques for Liver Segmentation,” IJARCET, vol. 4, no. 4, pp. 1661–1666, 2015.
  3. S. Editor, A. Gefen, and R. Aviv, '' Soft Tissue Biomechanical Modeling for Computer Assisted Surgery,'' Springer,2012.
  4. A. Bereciartua, A. Picon, A. Galdran, and P. Iriondo, “Automatic 3D model-based method for liver segmentation in MRI based on active contours and total variation minimization,” Biomed. Signal Process. Control, vol. 20, pp. 71–77, 2015.
  5. Y. Chen, Z. Wang, J. Hu, W. Zhao, and Q. Wu, “The domain knowledge based graph-cut model for liver CT segmentation,” Biomed. Signal Process. Control, vol. 7, no. 6, pp. 591–598, 2012.
  6. N. Aldeek, R. S. Alomari, M. B. Al-Zoubi, and H. Hiary, “Liver segmentation from abdomen CT images with bayesian model,” J. Theor. Appl. Inf. Technol., vol. 60, no. 3, pp. 483–490, 2014.
  7. D. Chi, Y. Zhao, and M. Li, “Automatic liver MR image segmentation with self-organizing map and hierarchical agglomerative clustering method,” Proc. - 2010 3rd Int. Congr. Image Signal Process. CISP 2010, vol. 3, pp. 1333–1337, 2010.
  8. Wolf, I., van Rikxoort, E., Raicu, D.S., Rau, A.M., Nemeth, G., Meinzer, H., Li, S., Li, R., Lennon, B., Lee, J., Lange, T., Lamecker, H., Rousson, M., Rusko, L., Wimmer, A., Waite, J., Susomboon, R., Soza, G., Sorantin, E., Slagmolen, P., Shimizu, A., Seghers, D., Schmidt, G., Saddi, K., Kobatake, H., Kitney, R., Kainmueller, D., Bello, F., Bekes, G., Beichel, R., Becker, C., Beck, A., Bauer, C., Aurich, V., Arzhaeva, Y., Styner, M., van Ginneken, B., Binnig, G., Bischof, H., Hornegger, J., Gre
  9. H. Masoumi, A. Behrad, M. A. Pourmina, and A. Roosta, “Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network,” Biomed. Signal Process. Control, vol. 7, no. 5, pp. 429–437, 2012.
  10. A. Rafiee, H. Masoumi, and A. Roosta, “Using neural network for liver detection in abdominal MRI images,” ICSIPA09 - 2009 IEEE Int. Conf. Signal Image Process. Appl. Conf. Proc., pp. 21–26, 2009.
  11. M. Moghbel, S. Mashohor, R. Mahmud, and M. I. Bin, “Automatic Liver Segmentation on Computed Tomography Using Random Walkers for Treatment Planning,” EXCLI, no. 2003, pp. 500–517, 2016.
  12. N. Khan and I. Ahmed, “Overview of Technical Elements of Liver Segmentation,” IJACSA, vol. 7, no. 12, pp. 271–278, 2016.
  13. S. Luo, X. Li, and J. Li, “Review on the Methods of Automatic Liver Segmentation from Abdominal Images,” J. Comput. Commun., vol. 2, no. 2, pp. 1–7, 2014.
  14. S. S. Kumar, R. S. Moni, and J. Rajeesh, “Automatic Segmentation of Liver and Tumor for CAD of Liver,” Advances, vol. 2, no. 1, pp. 63–70, 2011.
  15. K. H. Zou, W. M. Wells, R. Kikinis, and S. K. Warfield, “Three validation metrics for automated probabilistic image segmentation of brain tumours,” Stat. Med., vol. 23, no. 8, pp. 1259–1282, 2004.
  16. A. Hann et al., “Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound,” Sci. Rep., vol. 7, p. 12779, 2017.
  17. J. A. Mangai, J. Nayak, and V. S. Kumar, “A Novel Approach for Classifying Medical Images Using Data Mining Techniques,” Int. J. Comput. Sci. Electron. Eng., vol. 1, no. 2, 2013.
  18. R.Mallika, “Fraud Detection using Supervised Learning Algorithms,” Ijarcce, vol. 6, no. 6, pp. 6–10, 2017.
  19. S.Amidwar, “Plagiarism Detection Using Supervised Machine Learning,” JETIR, vol. 4, no. June, 2017.
  20. R. Blanco, I. Inza, M. Merino, J. Quiroga, and P. Larrañaga, “Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS,” J. Biomed. Inform., vol. 38, no. 5, pp. 376–388, 2005.
  21. K. H. Zou, S. K. Warfield, A. Bharatha, M. R. Kaus, S. J. Haker, W. M. Wells III, F. A. Jolesz, and R. Kikinis, “Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index,” Acad. Radiol., vol. 11, no. 2, pp. 178–189, 2004.
  22. A. M. Mharib, A. R. Ramli, S. Mashohor, and R. B. Mahmood, “Survey on liver CT image segmentation methods,” Artif. Intell. Rev., vol. 37, no. 2, pp. 83–95, 2012.
  23. Y. W. Chen, K. Tsubokawa, and A. H. Foruzan, “Liver Segmentation from Low Contrast Open MR Scans Using K-Means Clustering and Graph-Cuts,” vol. 54, no. February, 2016.
  24. M. Freiman, O. Eliassaf, Y. Taieb, L. Joskowicz, Y. Azraq, and J. Sosna, “An iterative Bayesian approach for nearly automatic liver segmentation: Algorithm and validation,” Int. J. Comput. Assist. Radiol. Surg., vol. 3, no. 5, pp. 439–446, 2008.
  25. K. Cheng, L. Gu, and J. Xu, “A Novel Shape Prior Based Level Set Method for Liver Segmentation From MR Images,” no. x, pp. 144–147, 2008.
  26. M. G. Linguraru, W. J. Richbourg, J. M. Watt, V. Pamulapati, and R. M. Summers, “Liver and tumor segmentation and analysis from CT of diseased patients via a generic affine invariant shape parameterization and graph cuts,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 7029 LNCS, pp. 198–206, 2012.
  27. S. J. Lim, Y. Y. Jeong, and Y. S. Ho, “Automatic liver segmentation for volume measurement in CT Images,” J. Vis. Commun. Image Represent., vol. 17, no. 4, pp. 860–875, 2006.
  28. K. Seo, “Automatic hepatic tumor segmentation using composite hypotheses,” Lect. notes Comput. Sci., vol. 3656, p. 922, 2005.
  29. E. Van Rikxoort, Y. Arzhaeva, and B. Van Ginneken, “Automatic segmentation of the liver in computed tomography scans with voxel classification and atlas matching,” Proc. MICCAI …, no. September, pp. 101–108, 2007.
  30. C. Platero and M. C. Tobar, “A multiatlas segmentation using graph cuts with applications to liver segmentation in CT scans,” Comput. Math. Methods Med., vol. 2014, 2014.
  31. R. T. Ribeiro, R. T. Marinho, and J. Miguel Sanches, “Classification and staging of chronic liver disease from multimodal data,” IEEE Trans. Biomed. Eng., vol. 60, no. 5, pp. 1336–1344, 2013.
  32. J. Li, C. Zhang, T. Wang, and Y. Zhang, “Generalized additive Bayesian network classifiers,” Proc. Int. Jt. Conf. Artif. …, no. x, pp. 913–918, 2007.
  33. M. Ceci, “Naive bayesian learning from structural data,” pp. 23–27, 2005.
  34. L. Li, “Bayesian Classification and Regression With High Dimensional Features,” 2007.
  35. R. V. de Schoot, D. Kaplan, J. Denissen, J. B. Asendorpf, F. J. Neyer, and M. A. G. van Aken, “A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research,” Child Dev., vol. 85, no. 3, pp. 842–860, 2014.
  36. M. Samsó, M. J. Palumbo, M. Radermacher, J. S. Liu, and C. E. Lawrence, “A Bayesian method for classification of images from electron micrographs,” J. Struct. Biol., vol. 138, no. 3, pp. 157–170, 2002.
  37. R.G.Mohamed, N. A. Seada, S.Hamdy, M.G.M.Mostafa, “Automatic Liver Segmentation from Abdominal MRI Images Using Active Contours.” IJCA, vol.176,no 1, October 2017.
Index Terms

Computer Science
Information Sciences

Keywords

Medical Image Analysis Automatic Liver Segmentation Bayesian Classifier.

Powered by PhDFocusTM