Document Type : Original Research

Authors

1 PhD Candidate, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

2 PhD, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

3 PhD, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran

4 PhD, Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran

10.31661/jbpe.v0i0.2103-1293

Abstract

Background: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis.
Objective: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm.
Material and Methods: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features.
Results: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms.
Conclusion: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.

Keywords

  1. Bereciartua A, Picon A, Galdran A, Iriondo P. 3D active surfaces for liver segmentation in multisequence MRI images. Comput Methods Programs Biomed. 2016;132:149-60. doi: 10.1016/j.cmpb.2016.04.028. PubMed PMID: 27282235.
  2. Massoptier L, Casciaro S. Fully automatic liver segmentation through graph-cut technique. Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5243-6, doi: 10.1109/IEMBS.2007.4353524. PubMed PMID: 18003190.
  3. Prasantha HS, Shashidhara HL, Murthy KN, Madhavi LG. Medical image segmentation. International Journal on Computer Science and Engineering. 2010;2(4):1209-18.
  4. Lebre MA, Vacavant A, Grand-Brochier M, Rositi H, Strand R, et al. A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities. Comput Med Imaging Graph. 2019;76:101635. doi: 10.1016/j.compmedimag.2019.05.003. PubMed PMID: 31301489.
  5. Sojar V, Stanisavljević D, Hribernik M, Glušič M, Kreuh D, Velkavrh U, Fius T. Liver surgery training and planning in 3D virtual space. International Congress Series. 2004;1268:390-4.
  6. López-Mir F, Naranjo V, Angulo J, Alcañiz M, Luna L. Liver segmentation in MRI: A fully automatic method based on stochastic partitions. Comput Methods Programs Biomed. 2014;114(1):11-28. doi: 10.1016/j.cmpb.2013.12.022. PubMed PMID: 24529637.
  7. Gloger O, Kühn J, Stanski A, Völzke H, Puls R. A fully automatic three-step liver segmentation method on LDA-based probability maps for multiple contrast MR images. Magn Reson Imaging. 2010;28(6):882-97. doi: 10.1016/j.mri.2010.03.010. PubMed PMID: 20409666.
  8. Liu H, Tang P, Guo D, Liu H, Zheng Y, Dan G. Liver MRI segmentation with edge-preserved intensity inhomogeneity correction. Signal, Image and Video Processing. 2018;12(4):791-8. doi: 10.1007/s11760-017-1221-5.
  9. Said S, Mostafa A, Houssein EH, Hassanien AE, Hefny H. Moth-flame optimization based segmentation for MRI liver images. International Conference on Advanced Intelligent Systems and Informatics. Cham: Springer; 2017. p. 320-30.
  10. Mostafa A, Hassanien AE, Houseni M, Hefny H. Liver segmentation in MRI images based on whale optimization algorithm. Multimedia Tools and Applications. 2017;76(23):24931-54. doi: 10.1007/s11042-017-4638-5.
  11. Huynh HT, Karademir I, Oto A, Suzuki K. Liver volumetry in MRI by using fast marching algorithm coupled with 3D geodesic active contour segmentation. Computational Intelligence in Biomedical; New York, NY: Springer; 2014. p. 141-57. doi: 10.1007/978-1-4614-7245-2_6.
  12. Masoumi H, Behrad A, Pourmina MA, Roosta A. Automatic liver segmentation in MRI images using an iterative watershed algorithm and artificial neural network. Biomedical Signal Processing and Control. 2012;7(5):429-37. doi: 10.1016/j.bspc.2012.01.002.
  13. Yuan Z, Wang Y, Yang J, Liu Y. A novel automatic liver segmentation technique for MR images. 3rd International Congress on Image and Signal Processing; Yantai, China: IEEE; 2010. p. 1282-86. doi: 10.1109/CISP.2010.5647676.
  14. Gloger O, Toennies K, Kuehn JP. Fully automatic liver volumetry using 3D level set segmentation for differentiated liver tissue types in multiple contrast MR datasets. Scandinavian Conference on Image Analysis; Berlin: Springer; 2011. p. 512-23. doi: 10.1007/978-3-642-21227-7_48.
  15. Platero C, Gonzalez M, Tobar MC, Poncela JM, Sanguino J, Asensio G, Santas E. Automatic method to segment the liver on multi-phase MRI. Computer Assisted Radiology and Surgery (CARS) 22nd International Congress and Exhibition; Barcelona, España: Matemática Aplicada; 2008.
  16. Takenaga T, Hanaoka S, Nomura Y, Nemoto M, Murata M, Nakao T, et al. Four-dimensional fully convolutional residual network-based liver segmentation in Gd-EOB-DTPA-enhanced MRI. Int J Comput Assist Radiol Surg. 2019;14(8):1259-66. doi: 10.1007/s11548-019-01935-z. PubMed PMID: 30929130.
  17. Kim IK, Jung DW, Park RH. Document image binarization based on topographic analysis using a water flow model. Pattern Recognition. 2002;35(1):265-77. doi: 10.1016/S0031-3203(01)00027-9.
  18. Oh HH, Lim KT, Chien SI. An improved binarization algorithm based on a water flow model for document image with inhomogeneous backgrounds. Pattern Recognition. 2005;38(12):2612-25. doi: 10.1016/j.patcog.2004.11.025.
  19. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics. 1979;9(1):62-6. doi: 10.1109/TSMC.1979.4310076.
  20. Haralick RM, Shanmugam K, Dinstein IH. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics. 1973;SMC-3(6):610-21. doi: 10.1109/TSMC.1973.4309314.
  21. Hagan MT, Demuth HB, Beale MH. Neural Network Design. Boston: PWS Publishing Company; 1995.