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Liver segmentation in MRI images based on whale optimization algorithm

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Abstract

This paper proposes an approach for liver segmentation in MRI images based on Whale optimization algorithm (WOA). It is used to extract the different clusters in the abdominal image to support the segmentation process. A statistical image is prepared to define the potential liver position in the abdominal image. Then, WOA divides the image into a predefined number of clusters. The prepared statistical image is converted into a binary image and multiplied by the image clustered by WOA. This multiplication process removes a great part of other organs from the image. It is followed by some points, picked up by user interaction, representing the required clusters which reside in the area of liver. The morphological operations enhance the initial segmented liver and produces the final image. The proposed approach is tested using a set of 70 MRI images, annotated and approved by radiology specialists. The resulting image is validated using structural similarity index measure (SSIM), similarity index (SI) and other five measures. The overall accuracy of the experimental result showed accuracy of 96.75% using SSIM and 97.5 using SI%.

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References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC Superpixels Compared To State of the Art Superpixel Methods, IEEE Transactions on Pattern analysis and Machine Intelligence, vol. 34, no. 11

  2. Ali A, Couceiro M, Hassenian A (2014) Towards an optimal number of clusters using a nested particle swarm approach for liver CT image segmentation. In: Advanced machine learning technologies and applications, vol 488, pp 331–343

  3. Azizi R (2014) Empirical Study of Artificial Fish Swarm Algorithm, International Journal of Computing, Communications and Networking, Vol. 3, No.1

  4. Ben George E, Karnan M (2012) MR Brain Image Segmentation using Bacteria Foraging Optimization Algorithm. International journal of engineering and technology (IJET), Vol. 4 No. 5

  5. Brodersen KH, Soon Ong C, Stephan KE, Buhmann JM (2010) The Balanced Accuracy and its Posterior Distribution. In: 20th International Conference on Pattern Recognition, Istanbul, Turkey

    Google Scholar 

  6. Chen E, Chung P, Chen Tsai H, Chang C (1998) An automatic diagnostic system for CT liver image classification. IEEE Trans Biomed Eng 45(6):783–794

    Article  Google Scholar 

  7. Dorigoa M, Blum C (2005) Ant Colony Optimization Theory: A survey. Theor Comput Sci 344:243–278

    Article  MathSciNet  MATH  Google Scholar 

  8. George JR, Jeba A, Kumari D (2014) Segmentation and Analysis of Lung Cancer Images Using Optimization Technique, International Journal of Engineering and Innovative Technology (IJEIT) Vol. 3, Issue 10

  9. Horng M (2011) Multilevel thresholding selection based on the artificial bee colony aalgorithm for image segmentation. Expert Syst Appl 38(11):13785–13791

    Google Scholar 

  10. Hassanien AE, Alamry E (2015) Swarm intelligence: Principles, Advances, and Applications, CRC–Taylor & Francis Group

  11. Junrui L (2015) An Improved SLIC Superpixels using Reciprocal Nearest Neighbour Clustering. International journal of signal processing, Image Processing and Pattern Recognition 8(5):239–248

    Article  Google Scholar 

  12. Karaboga D, BasturkAffiliated B, University E Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems, Foundations of Fuzzy Logic and Soft Computing, Volume 4529 of the series Lecture Notes in Computer Science pp 789–798

  13. Karaboga D, Akay B (2009) A Comparative Study of Artificial Bee Colony algorithm. Appl Math Comput 214:108–132

    MathSciNet  MATH  Google Scholar 

  14. Kaur D, Kaur Y (2014) Intelligent medical image segmentation using FCM, GA and PSO, International Journal of Computer Science and Information Technologies, Vol. 5

  15. Liu X, Wang X, Shi N, Li C (2014) Image segmentaion algorithm based on improved ant colony algorithm, international journal of signal processing. Image Processing and Pattern Recognition 7(3):433–442

    Article  Google Scholar 

  16. Liang Y, Yin Y (2013) A new multilevel thresholding approach based on the ant colony system and the EM algorithm, international journal of innovative computing, information and control, Vol. 9 No. 1

  17. Luo Q, Ouyang Z, Chen X, Zhou Y (2014) A multilevel threshold image segmentation algorithm based on glowworm swarm optimization. J Comput Inf Syst 10(4):1621–1628

    Google Scholar 

  18. Madhava Raja NS, Rajinikanth V, Latha K (2014) Otsu based optimal multilevel image thresholding using firefly algorithm. Modelling and Simulation in Engineering, Hindawi

    Google Scholar 

  19. Miguel Carreira-Perpinan A (2015) A Review of Mean-shift Algorithms for Clustering, Handbook of Cluster Analysis CRC

  20. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  21. Mandal D, Chatterjee A, Maitra M (2014) Robust Medical Image Segmentation using Particle Swarm Optimization Aided Level Set-based Global Fitting Energy Active Contour Approach. Eng Appl Artif Intell 35:199–214

    Article  Google Scholar 

  22. Mostafa A, Ghali NI, Hassanien A, Hefny H, Schaefer G (2012) Evaluating the effects of image filters in CT Liver CAD system, International Conference on Biomedical and Health Informatics China

  23. Mostafa A, Elfattah M, Fouad A, Hassanien A, Hefny H (2015) Wolf Local Thresholding Approach for Liver Image Segmentation in CT Images, International Afro-European conference for industrial advancement AECIA, Addis Ababa, Ethiopia

  24. Fouad Ali A, Mostafa A, Ismail Sayed G, Abd Elfattah M, Hassanien A (2016) Nature inspired optimization algorithms for CT liver segmentation, medical imaging in clinical applications:- algorithmic and Computer-Based approaches, Springer

  25. Mostafa A, Elfattah M, Fouad A, Hassanien A, Hefny H (2015) Enhanced Region Growing Segmentation For CT Liver Images. In: The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), Beni Suef, Egypt

    Google Scholar 

  26. Mostafa A, Abd Elfattah M, Fouad A, Hassanien A, Kim T (2015) Region Growing Segmentation with Iterative K-means for CT Liver Images, International Conference on Advanced Information Technology and Sensor Application (AITS) China

  27. Mostafa A, Fouad A, Abd M, Elfattah A, Ella Hassanien H, Hefny S, Zhue G, Schaeferf G (2015) CT Liver Segmentation using Artificial Bee Colony Optimisation. In: 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems, Procedia Computer Science 60, Singapore, pp 1622–1630

    Google Scholar 

  28. Nelson AL, Barlow GJ, Doitsidis L (2009) Fitness functions in evolutionary robotics: a survey and analysis. Robot Auton Syst 57:345–370

    Article  Google Scholar 

  29. Neumuller C, Wagner S, Kronberger G, Affenzeller M (2011) Parameter Meta-Optimization of metaheuristic optimization algorithms. Computer aided systems theory of the series Lecture Notes in Computer Science 6927:367–374

    Article  Google Scholar 

  30. Oliveira D, Feitosa RQ, Correia MM (2011) Segmentation of liver, its vessels and lesions from CT images for surgical planning. Biomed Eng Online:10–30. doi:10.1186/1475-925X-10-30

  31. Robinson PJA, Ward J (2006) MRI Of the Liver-A Practical Guide. Taylor CRC & Francis, New Yourk, USA

    Book  Google Scholar 

  32. Sankari L (August 2014) Image segmentation using glowworm swarm optimization for finding initial seed. International Journal of Science and Research (IJSR), Volume 3 Issue 8

  33. Sivaramakrishnan A, Karnan M Medical Image Segmentation Using Firefly Algorithm and Enhanced Bee Colony Optimization. In: International Conference on Information and Image Processing (ICIIP-201), China, pp 316–321

  34. Sunny S, Pratheba M (2014) Detection of Breast Cancer using Firefly Algorithm. International Journal of Emerging Technologies and Engineering (IJETE) Vol. 1 Issue 3

  35. Szeliski R (2014) Computer vision: Algorithms and Applications, Springer

  36. Talbi EG (2009) Metaheuristics from Design to Implementation, John Wiley & Sons

  37. Thiagarajan B, Bremananth R (2014) Brain Image Segmentation Using Conditional Random Field Based On modified artificial bee colony optimization algorithm, international journal of medical, health, biomedical, bioengineering and pharmaceutical engineering vol. 8 no. 9

  38. Wang J, Han S, Shen N (2014) Improved GSO optimized ESN Soft-Sensor model of flotation process based on multisource heterogeneous information fusion, The Scientific World Journal, Hindawi

  39. Wang Z, Bovik AC, Sheikh HR, Simoncelli P (2004) Image Quality Assessment: From Error Measurement to Structural Similarity, IEEE Transactions on image Processing, vol. 13, no. 1

  40. Wu G, Zhao X, Luo S, Shi H (2015) Histological image segmentation using fast mean shift clustering method. BioMedical Engineering OnLine 14–24

  41. Xian-hua J, Yuan-qing Z, Medical DR (2013) Image Enhancement Processing Method Based on Artificial Fish-Swarm Algorithm, International Journal of Digital Content Technology & its Applications, Vol. 7 Issue 4

  42. Xuechen L, Luo S, Jaming L (2013) Liver segmentation from CT image using fuzzy clustering and level set. Journal of Signal and Information Processing 4(3):36–42

    Article  Google Scholar 

  43. Yang Z (2010) Nature Inspired Metaheuristic algorithms, 2nd Edition Luniver press

  44. Zidan A, Ghali NI, Hassanien A, Hefny H (2013) Level Set-based CT Liver Computer Aided Diagnosis System. International Journal of Imaging and Robotics, Vol. 9

  45. Zidan A, Ghali NI, Hassanien A, Hefny H (2012) Level Set-based CT Liver Image Segmentation with Watershed and Artificial Neural Networks, 12th International Conference on Hybrid Intelligent Systems (HIS) India

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Authors and Affiliations

  1. Institute of Statistical Studies and Research, Cairo University, Giza, Egypt

    Abdalla Mostafa & Hesham Hefny

  2. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. National Liver Institute, Radiology Department, Menofia University, Shebeen El-Kom, Egypt

    Mohamed Houseni

  4. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Abdalla Mostafa & Aboul Ella Hassanien

  5. Computer Department, Institute of Statistical Studies and Research (ISSR), 5 Zewail st., Giza, Egypt

    Abdalla Mostafa

Authors
  1. Abdalla Mostafa
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  2. Aboul Ella Hassanien
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  3. Mohamed Houseni
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  4. Hesham Hefny
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Correspondence to Abdalla Mostafa.

Additional information

Abdalla Mostafa and Aboul Ella Hassanien are both member of Scientific Research Group in Egypt (SRGE).

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Mostafa, A., Hassanien, A.E., Houseni, M. et al. Liver segmentation in MRI images based on whale optimization algorithm. Multimed Tools Appl 76, 24931–24954 (2017). https://doi.org/10.1007/s11042-017-4638-5

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  • DOI: https://doi.org/10.1007/s11042-017-4638-5

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