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Biomedical Image Segmentation: A Survey

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Abstract

Medical Image Segmentation is the process of segmenting and detecting boundaries of anatomical structures in various types of 2D and 3D-medical images. The latter come from different modalities, such as Magnetic Resonance Imaging (MRI), X-Rays, Positron Emission Tomography (PET)/Single-Photon Emission Computed Tomography, Computed Tomography (CT), and Ultrasound (US). It is a key supporting technology for medical applications including diagnostics, planning, monitoring, and guidance. Hence, a large number of segmentation methods have been published in past decades. This paper presents a comprehensive review of the current medical segmentation techniques. In particular, we reviewed the most important medical segmentation methods that have been utilized for almost all types of medical images. We grouped these methods into categories and then compared, contrasted, and highlighted their main advantages and limitations.

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References

  1. Ahmmed R, Rahman MA, Hossain MF. Fuzzy logic based algorithm to classify tumor categories with position from brain MRI images. In: 2017 3rd International Conference on electrical information and communication technology (EICT), 2017; p. 1–6.

  2. Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep learning for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449–59.

    Google Scholar 

  3. Al Sharif SM, Deriche M, Maalej N, El Ferik S. A fast geodesic active contour model for medical image segmentation using prior analysis and wavelets. Arab J Sci Eng. 2014;39(2):1017–37.

    Google Scholar 

  4. Albà X, Lekadir K, Pereanez M, Medrano-Gracia P, Young AA, Frangi AF. Automatic initialization and quality control of large-scale cardiac MRI segmentations. Med Image Anal. 2018;43:129–41.

    Google Scholar 

  5. Ali H, Badshah N, Chen K, Khan GA. A variational model with hybrid images data fitting energies for segmentation of images with intensity inhomogeneity. Pattern Recognit. 2016;51:27–42.

    MATH  Google Scholar 

  6. Alom MZ, Hasan M, Yakopcic C, Taha TM. Asari VK. Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. 2018. arXiv preprint arXiv:1802.06955.

  7. Amini AA, Weymouth TE, Jain RC. Using dynamic programming for solving variational problems in vision. IEEE Trans Pattern Anal Mach Intell. 1990;12(9):855–67.

    Google Scholar 

  8. Ammari H. An introduction to mathematics of emerging biomedical imaging. Berlin: Springer; 2008.

    MATH  Google Scholar 

  9. Aneja D, Rawat TK. Fuzzy clustering algorithms for effective medical image segmentation. Int J Intell Syst Appl. 2013;5(11):55.

    Google Scholar 

  10. Angelova D, Mihaylova L. Contour segmentation in 2d ultrasound medical images with particle filtering. Mach Vis Appl. 2011;22(3):551–61.

    Google Scholar 

  11. Argenti F, Alparone L, Benelli G. Fast algorithms for texture analysis using co-occurrence matrices. In: IEE Proceedings F (Radar and Signal Processing), 1990; vol. 137, p. 443–48. IET.

  12. Aslam A, Khan E, Beg MS. Improved edge detection algorithm for brain tumor segmentation. Proc Comput Sci. 2015;58:430–7.

    Google Scholar 

  13. Asman AJ, DeLisi MP, Mawn LA, Galloway RL, Landman BA. Robust non-local multi-atlas segmentation of the optic nerve. In: Ourselin S, Haynor DR, editors. Medical imaging 2013: image processing, vol. 8669. Washington, DC: International Society for Optics and Photonics; 2013. p. 434–41. http://www.spiedigitallibrary.org/conference-proceedings-of-spie/8669/86691L/Robust-non-local-multi-atlas-segmentation-of-the-optic-nerve/10.1117/12.2007015.short?SSO=1.

    Google Scholar 

  14. Attia A, Dayan S. Detection and segmentation of the left ventricle in cardiac MRI using deep learning. 2018. arXiv preprint arXiv:1801.02171.

  15. Avşar TS, Arıca S. Automatic segmentation of computed tomography images of liver using watershed and thresholding algorithms. In: Eskola H, Väisänen O, Viik J, Hyttinen J, editors. EMBEC & NBC 2017. Singapore: Springer Singapore; 2018. p. 414–7.

    Google Scholar 

  16. Azencott R, Wang JP, Younes L. Texture classification using windowed Fourier filters. IEEE Trans Pattern Anal Mach Intell. 1997;19(2):148–53.

    Google Scholar 

  17. Badrinarayanan V, Handa A, Cipolla R. Segnet: a deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. 2015. arXiv preprint arXiv:1505.07293.

  18. Bajcsy P, Cardone A, Chalfoun J, Halter M, Juba D, Kociolek M, Majurski M, Peskin A, Simon C, Simon M, et al. Survey statistics of automated segmentations applied to optical imaging of mammalian cells. BMC Bioinform. 2015;16(1):330.

    Google Scholar 

  19. Bakir H, Charfi M, Zrida J. Automatic active contour segmentation approach via vector field convolution. Signal Image Video Process. 2016;10(1):9–18.

    Google Scholar 

  20. Beevi S, Nair MS, Bindu G. Automatic segmentation of cell nuclei using krill herd optimization based multi-thresholding and localized active contour model. Biocybern Biomed Eng. 2016;36(4):584–96.

    Google Scholar 

  21. Belkacem-Boussaid K, Sertel O, Lozanski G, Shana’aah A, Gurcan M. Extraction of color features in the spectral domain to recognize centroblasts in histopathology. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009; p. 3685–688.

  22. Bernal J, Kushibar K, Cabezas M, Valverde S, Oliver A, Lladó X. Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging. IEEE Access. 2019;7:89986–90002.

    Google Scholar 

  23. Bieniecki W. Oversegmentation avoidance in watershed-based algorithms for color images. In: Proceedings of the International Conference modern problems of radio engineering, telecommunications and computer science. 2004; p. 169–72.

  24. Boykov YY, Jolly M. Interactive graph cuts for optimal boundary region segmentation of objects in n-d images. In: Proceedings Eighth IEEE International Conference on computer vision. ICCV 2001, 2001; vol. 1, pp. 105–112.

  25. Caselles V, Catté F, Coll T, Dibos F. A geometric model for active contours in image processing. Numer Math. 1993;66(1):1–31.

    MathSciNet  MATH  Google Scholar 

  26. Changchun S, Hengguo Y, Xiaoyun L, Wufan C. Medical image segmentation based on level set with new local fitting energy. In: 2010 International Conference of medical image analysis and clinical application, 2010; pp. 34–37.

  27. Charfi M, Zrida J. Speed improvement of b-snake algorithm using dynamic programming optimization. IEEE Trans Image Process. 2011;20(10):2848–55.

    MathSciNet  MATH  Google Scholar 

  28. Chaubey AK. Comparison of the local and global thresholding methods in image segmentation. World J Res Rev. 2016;2(1):1–4.

    Google Scholar 

  29. Chaudhuri BB, Sarkar N. Texture segmentation using fractal dimension. IEEE Trans Pattern Anal Mach Intell. 1995;17(1):72–7.

    Google Scholar 

  30. Chen T, Huang TS. Region based hidden Markov random field model for brain MR image segmentation. In: WEC (2), 2005; p. 233–236. Citeseer.

  31. Chen X, Pan L. A survey of graph cuts/graph search based medical image segmentation. IEEE Rev Biomed Eng. 2018;11:112–24.

    Google Scholar 

  32. Chen Y, Huang F, Tagare HD, Rao M. A coupled minimization problem for medical image segmentation with priors. Int J Comput Vis. 2007;71(3):259–72.

    Google Scholar 

  33. Chenyang X, Prince JL. Gradient vector flow: a new external force for snakes. In: Proceedings of IEEE Computer Society Conference on computer vision and pattern recognition, 1997; p. 66–71.

  34. Chetih N, Messali Z, Serir A, Ramou N. Robust fuzzy c-means clustering algorithm using non-parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation. IET Image Process. 2017;12(5):652–60.

    Google Scholar 

  35. Chu A, Sehgal CM, Greenleaf JF. Use of gray value distribution of run lengths for texture analysis. Pattern Recognit Lett. 1990;11(6):415–9.

    MATH  Google Scholar 

  36. Chunming L, Chenyang X, Changfeng G, Fox MD. Level set evolution without re-initialization: a new variational formulation. In: 2005 IEEE Computer Society Conference on computer vision and pattern recognition (CVPR’05), 2005; vol. 1, p. 430–36.

  37. Cootes TF, Taylor CJ, Cooper DH, Graham J. Active shape models-their training and application. Comput Vis Image Underst. 1995;61(1):38–59.

    Google Scholar 

  38. Davies R, Twining C, Taylor C. Statistical models of shape and appearance. London: Springer London; 2008. p. 1–39.

    MATH  Google Scholar 

  39. Dayhoff JE, DeLeo JM. Artificial neural networks: opening the black box. Cancer Interdiscipl Int J Am Cancer Soc. 2001;91(S8):1615–35.

    Google Scholar 

  40. Depa M, Sabuncu MR, Holmvang G, Nezafat R, Schmidt EJ, Golland P. Robust atlas-based segmentation of highly variable anatomy: left atrium segmentation. In: Statistical atlases and computational models of the heart. Springer Berlin Heidelberg, Berlin, Heidelberg, 2010; p. 85–94. http://link.springer.com/chapter/10.1007/978-3-642-15835-3_9.

  41. Derraz F, Beladgham M, Khelif M. Application of active contour models in medical image segmentation. In: International Conference on information technology: coding and computing, 2004. Proceedings. ITCC 2004, 2004; vol. 2, p. 675–81.

  42. Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med. 2015;2015:23. https://doi.org/10.1155/2015/450341.

    Article  Google Scholar 

  43. Devalla SK, Renukanand PK, Sreedhar BK, Perera S, Mari JM, Chin KS, Tun TA, Strouthidis NG, Aung T, Thiéry AH.,et al. Drunet: a dilated-residual u-net deep learning network to digitally stain optic nerve head tissues in optical coherence tomography images. 2018. arXiv preprint arXiv:1803.00232.

  44. Dollár KHGGP, Girshick R. Mask R-CNN. Facebook AI Research (FAIR). 2017.

  45. Drozdzal M, Chartrand G, Vorontsov E, Shakeri M, Di Jorio L, Tang A, Romero A, Bengio Y, Pal C, Kadoury S. Learning normalized inputs for iterative estimation in medical image segmentation. Med Image Anal. 2018;44:1–13.

    Google Scholar 

  46. Duta N, Sonka M. Segmentation and interpretation of MR brain images. an improved active shape model. IEEE Trans Med Imaging. 1998;17(6):1049–62.

    Google Scholar 

  47. Duth PS, Vipuldas CA. Saikrishnan VP. Integrated spatial fuzzy clustering with variational level set method for MRI brain image segmentation. In: 2017 International Conference on communication and signal processing (ICCSP), 2017; p. 1559–562.

  48. Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, Vembar M, Olszewski ME, Subramanyan K, Lavi G, et al. Automatic model-based segmentation of the heart in CT images. IEEE Tans Med Imaging. 2008;27(9):1189–201.

    Google Scholar 

  49. Egmont-Petersen M, de Ridder D, Handels H. Image processing with neural networks—a review. Pattern Recognit. 2002;35(10):2279–301.

    MATH  Google Scholar 

  50. Elangovan A, Jeyaseelan T. Medical imaging modalities: a survey. In: 2016 International Conference on emerging trends in engineering, technology and science (ICETETS), 2016; p. 1–4.

  51. Elnakib A, Gimelfarb G, Suri JS, El-Baz A. Medical image segmentation: a brief survey. New York: Springer New York; 2011. p. 1–39.

    Google Scholar 

  52. Morais P, Vila ̧ca, JL, Queir ́os S, Bourier F, Deisenhofer I, Tavares JMR, D’hooge J. A competitive strategy for atrial and aortic tract segmentation based on deformable models. Medical Image Analysis. 2017;42:102–16. https://doi.org/10.1016/j.media.2017.07.007, http://www.sciencedirect.com/science/article/pii/S1361841517301159.

  53. Friese KI, Blanke P, Wolter FE. Yadiv—an open platform for 3d visualization and 3d segmentation of medical data. Vis Comput. 2011;27(2):129–39.

    Google Scholar 

  54. Ge X, Tian J. An automatic active contour model for multiple objects. In: Object recognition supported by user interaction for service robots, vol. 2. 2002; p. 881–84. http://ieeexplore.ieee.org/document/1048444.

  55. Gour N, Khanna P. Blood vessel segmentation using hybrid median filtering and morphological transformation. In: 2017 13th International Conference on signal-image technology internet-based systems (SITIS), 2017; p. 151–157.

  56. Grau V, Mewes A, Alcaniz M, Kikinis R, Warfield SK. Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging. 2004;23(4):447–58.

    Google Scholar 

  57. Grimson WEL, Ettinger G, Kapur T, Leventon ME, Wells WM III, Kikinis R. Utilizing segmented MRI data in image-guided surgery. Int J Pattern Recognit Artif Intell. 1997;11(08):1367–97.

    Google Scholar 

  58. Guerrout EH, Mahiou R, Ait-Aoudia S. Medical image segmentation on a cluster of pcs using Markov random fields. Int J New Comput Arch Appl (IJNCAA). 2013;3(1):35–44.

    Google Scholar 

  59. Hahn HK, Peitgen HO. The skull stripping problem in MRI solved by a single 3d watershed transform. In: International Conference on medical image computing and computer-assisted intervention, 2000; pp. 134–43. Springer.

  60. Hanbury A. Image segmentation by region based and watershed algorithms. Wiley Encycl Comput Sci Eng. 2007;77:1543–52.

    Google Scholar 

  61. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2016; p. 770–78.

  62. Held K, Kops ER, Krause BJ, Wells WM, Kikinis R, Muller-Gartner HW. Markov random field segmentation of brain MR images. IEEE Trans Med Imaging. 1997;16(6):878–86.

    Google Scholar 

  63. Hu GM. Survey of recent volumetric medical image segmentation techniques. In: de Mello CAB, editor. Biomedical engineering, chap. 17. Rijeka: IntechOpen; 2009.

    Google Scholar 

  64. Hua P, Song Q, Sonka M, Hoffman EA, Reinhardt JM. Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm. In: 2011 IEEE International Symposium on biomedical imaging: from nano to macro, 2011; p. 2072–75.

  65. Huang Q, Luo Y, Zhang Q. Breast ultrasound image segmentation: a survey. Int J Comput Assist Radiol Surg. 2017;12(3):493–507.

    Google Scholar 

  66. Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach. Proc Comput Sci. 2017;120:580–7.

    Google Scholar 

  67. Joshi S, Kaziska D, Srivastava A, Mio W. Riemannian structures on shape spaces: a framework for statistical inferences. Boston: Birkhäuser Boston; 2006. p. 313–33.

    MATH  Google Scholar 

  68. Kalshetti P, Bundele M, Rahangdale P, Jangra D, Chattopadhyay C, Harit G, Elhence A. An interactive medical image segmentation framework using iterative refinement. Comput Biol Med. 2017;83:22–33.

    Google Scholar 

  69. Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis. 1988;1(4):321–31.

    MATH  Google Scholar 

  70. Kattire S, Shah A. Boundary detection algorithm implementation for medical images. Int J Eng Res Technol (IJERT). 2014;3(12):0181–2278.

    Google Scholar 

  71. Khadidos A, Sanchez V, Li CT. Weighted level set evolution based on local edge features for medical image segmentation. IEEE Trans Image Process. 2017;26(4):1979–91.

    MathSciNet  MATH  Google Scholar 

  72. Kitrungrotsakul T, Han XH, Iwamoto Y, Chen YW. Automatic and robust vessel segmentation in CT volumes using submodular constrained graph. In: International Conference on innovation in medicine and healthcare. Springer; 2017, p. 57–66.

  73. Krinidis S, Chatzis V. A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process. 2010;19(5):1328–37.

    MathSciNet  MATH  Google Scholar 

  74. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, 2012; p. 1097–1105. http://dl.acm.org/doi/abs/10.1145/3065386.

  75. Lao J, Chen Y, Li ZC, Li Q, Zhang J, Liu J, Zhai G. A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme. Sci Rep. 2017;7(1):1–8.

    Google Scholar 

  76. Leventon ME, Grimson WEL, Faugeras O. Statistical shape influence in geodesic active contours. In: 5th IEEE EMBS International Summer School on Biomedical Imaging, 2002., pp. 8–pp. IEEE 2002.

  77. Li B, Acton ST. Automatic active model initialization via Poisson inverse gradient. IEEE Trans Image Process. 2008;17(8):1406–20.

    MathSciNet  Google Scholar 

  78. Li C, Xu C, Gui C, Fox MD. Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process. 2010;19(12):3243–54.

    MathSciNet  MATH  Google Scholar 

  79. Li G, Sanchez V, Patel G, Quenby S, Rajpoot N. Localisation of luminal epithelium edge in digital histopathology images of IHC stained slides of endometrial biopsies. Comput Med Imaging Graph. 2015;42:56–64.

    Google Scholar 

  80. Li S. Markov random field modeling in computer vision springer. In: Tokyo 95, p. 1995. 1995.

  81. Li Y, Chi Z. MR brain image segmentation based on self-organizing map network. Int J Inf Technol. 2005;11(8):45–53.

    Google Scholar 

  82. Liu J, Wang Z, Zhang R. Liver cancer CT image segmentation methods based on watershed algorithm. In: 2009 International Conference on computational intelligence and software engineering, 2009; p. 1–4.

  83. Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T. Deep learning in medical ultrasound analysis: a review. Engineering. 2019;5(2):261–75.

    Google Scholar 

  84. Lu SY, Fu KS. A syntactic approach to texture analysis. Tech. rep.: Purdue Univ Lafayette Ind School of Electrical Engineering; 1977.

  85. MeindertNiemeijer XC, Lee LZK, Abràmoff MD, Sonka M. 3d segmentation of fluid-associated abnormalities in retinal oct: probability constrained graph-search-graph-cut. IEEE Trans Med Imaging. 2012;31(8):1521–31.

    Google Scholar 

  86. Menet S. B-snakes: implementation and application to stereo. In: Proceedings of Third International Conference on computer vision, 1990 (1990).

  87. Mharib AM, Ramli AR, Mashohor S, Mahmood RB. Survey on liver CT image segmentation methods. Artif Intell Rev. 2012;37(2):83–95.

    Google Scholar 

  88. Moftah HM, Azar AT, Al-Shammari ET, Ghali NI, Hassanien AE, Shoman M. Adaptive k-means clustering algorithm for MR breast image segmentation. Neural Comput Appl. 2014;24(7–8):1917–28.

    Google Scholar 

  89. Mojtabavi A, Farnia P, Ahmadian A, Alimohamadi M, Pourrashidi A, Ra, HS, Alirezaie J. Segmentation of GBM in mri images using an efficient speed function based on level set method. In: 2017 10th International Congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), 2017; pp. 1–6.

  90. Mudukshiwale AD, Patil Y. Brain tumor detection using digital image processing. Brain. 2019;6(05).

  91. Mustaqeem A, Javed A, Fatima T. An efficient brain tumor detection algorithm using watershed & thresholding based segmentation. Int J Image Graph Signal Process. 2012;4(10):34.

    Google Scholar 

  92. Ng H, Ong S, Foong K, Nowinski W. An improved watershed algorithm for medical image segmentation. In: Proceedings 12th International Conference on biomedical engineering. 2005.

  93. Niblac W. An introduction to digital image processing, 1986. In: Leedham G, Yan, C, Takru K, Tan J, Mian L editors. Comparison of some thresholding algorithms for text/background segmentation in difficult document images. In Proceedings of the Seventh International Conference on Document Analysis and Recognition, 2003; p. 859–64.

  94. Park H, Bland PH, Meyer CR. Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Trans Med Imaging. 2003;22(4):483–92.

    Google Scholar 

  95. Park J, Park S, Cho W. Medical image segmentation using level set method with a new hybrid speed function based on boundary and region segmentation. IEICE Trans Inf Syst. 2012;95(8):2133–41.

    Google Scholar 

  96. Park JG, Lee C. Skull stripping based on region growing for magnetic resonance brain images. NeuroImage. 2009;47(4):1394–407.

    Google Scholar 

  97. Parvati K, Rao P, Mariya Das M. Image segmentation using gray-scale morphology and marker-controlled watershed transformation. Discrete Dyn Nat Soc. 2008;2008:8. https://doi.org/10.1155/2008/384346.

    Article  MathSciNet  MATH  Google Scholar 

  98. Patino L. Fuzzy relations applied to minimize over segmentation in watershed algorithms. Pattern Recognit Lett. 2005;26(6):819–28.

    Google Scholar 

  99. Pratondo A, Chui CK, Ong SH. Integrating machine learning with region-based active contour models in medical image segmentation. J Vis Commun Image Represent. 2017;43:1–9.

    Google Scholar 

  100. Qian X, Wang J, Guo S, Li Q. An active contour model for medical image segmentation with application to brain CT image. Med Phys. 2013;40(2):021911.

    Google Scholar 

  101. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF, editors. Medical image computing and computer-assisted intervention—MICCAI 2015. Cham: Springer International Publishing; 2015. p. 234–41.

    Google Scholar 

  102. Sahu M, Parvathi K, Krishna MV. Parametric comparison of k-means and adaptive k-means clustering performance on different images. Int J Electric Comput Eng. 2017;7(2):810.

    Google Scholar 

  103. Sandhya G, Babu Kande G, Savithri TS. Multilevel thresholding method based on electromagnetism for accurate brain MRI segmentation to detect white matter, gray matter, and CSF. BioMed Res Int. 2017;2017:17. https://doi.org/10.1155/2017/6783209.

    Article  Google Scholar 

  104. Sauvola J, Pietikäinen M. Adaptive document image binarization. Pattern Rcognit. 2000;33(2):225–36.

    Google Scholar 

  105. Senthilkumaran N, Vaithegi S. Image segmentation by using thresholding techniques for medical images. Comput Sci Eng Int J. 2016;6(1):1–13.

    Google Scholar 

  106. Shah BN, Shah SK, Kosta YP. A seeded region growing algorithm for spot detection in medical image segmentation. In: 2011 International Conference on image information processing, 2011; pp. 1–4.

  107. Shah M. Fundamentals of computer vision. Orlando: University of Central Florida; 1997.

    Google Scholar 

  108. Shen D, Zhan Y, Davatzikos C. Segmentation of prostate boundaries from ultrasound images using statistical shape model. IEEE Trans Med Imaging. 2003;22(4):539–51.

    Google Scholar 

  109. Shree NV, Kumar T. Identification and classification of brain tumor MRI images with feature extraction using dwt and probabilistic neural network. Brain Inf. 2018;5(1):23–30.

    Google Scholar 

  110. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. arXiv preprint arXiv:1409.1556.

  111. Singh TR, Roy S, Singh OI, Sinam T, Singh K, et al. A new local adaptive thresholding technique in binarization. 2012. arXiv preprint arXiv:1201.5227.

  112. Smith SM. Fast robust automated brain extraction. Human Brain Map. 2002;17(3):143–55.

    Google Scholar 

  113. Song B, Sacan A. Automated wound identification system based on image segmentation and artificial neural networks. In: 2012 IEEE International Conference on bioinformatics and biomedicine, 2012; p. 1–4. IEEE.

  114. Soomro S, Munir A, Choi KN. Hybrid two-stage active contour method with region and edge information for intensity inhomogeneous image segmentation. PLoS One. 2018;13(1):1–9.

  115. Soudani A, Zagrouba E. Adaptive region based active contour model for image segmentation. In: 2017 IEEE/ACS 14th International Conference on computer systems and applications (AICCSA), 2017; p. 717–24.

  116. Stich M, Vogt J, Lindner M, Ringler R. Implementation and evaluation of segmentation algorithms according to multimodal imaging in personalized medicine. Curr Dir Biomed Eng. 2017;3(2):207–10.

    Google Scholar 

  117. Su L, Fu X, Zhang X, Cheng X, Ma Y, Gan Y, Hu Q. Delineation of carpal bones from hand x-ray images through prior model, and integration of region-based and boundary-based segmentations. IEEE Access. 2018;6:19993–20008.

    Google Scholar 

  118. Swierczynski P, Papież BW, Schnabel JA, Macdonald C. A level-set approach to joint image segmentation and registration with application to ct lung imaging. Comput Med Imaging Graph. 2018;65:58–68.

    Google Scholar 

  119. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, 2015; pp. 1–9.

  120. Taha AA, Hanbury A. Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool. BMC Med Imaging. 2015;15(1):29.

    Google Scholar 

  121. Tan S, Li L, Choi W, Kang MK, DSouza D, Lu W. Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18f-fdg pet. Phys Med Biol. 2017;62(13):5383.

    Google Scholar 

  122. Tauber C, Batatia H, Ayache A. A general quasi-automatic initialization for snakes: application to ultrasound images. In: IEEE International Conference on image processing 2005, vol. 2, pp. II–806, 2005.

  123. Taylor P. Computer aids for decision-making in diagnostic radiology-a literature review. Br J Radiol. 1995;68(813):945–57.

    Google Scholar 

  124. Thangaraj S, Periyasamy V, Balaji R. Retinal vessel segmentation using neural network. IET Image Process. 2017;12(5):669–78.

    Google Scholar 

  125. Tjahyaningtijas HPA. Brain tumor image segmentation in MRI image. In: IOP Conference series: materials science and engineering, vol. 336, p. 012012. IOP Publishing. 2018.

  126. Tor-Díez C, Passat N, Bloch I, Faisan S, Bednarek N, Rousseau F. An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Comput Med Imaging Graph. 2018;70:73–82.

    Google Scholar 

  127. Trichili H, Bouhlel M, Derbel N, Kamoun L. A survey and evaluation of edge detection operators application to medical images. In: IEEE International Conference on systems, man and cybernetics, vol. 4, pp. 4 pp. vol. 4, 2002.

  128. Tsai A, Wells W, Warfield SK, Willsky A. Level set methods in an EM framework for shape classification and estimation. In: Barillot C, Haynor DR, Hellier P, editors. Medical image computing and computer-assisted intervention—MICCAI 2004. Berlin: Springer Berlin Heidelberg; 2004. p. 1–9.

    Google Scholar 

  129. Tsai A, Yezzi A, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A. A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans Med Imaging. 2003;22(2):137–54.

    Google Scholar 

  130. Tutar IB, Pathak SD, Gong L, Cho PS, Wallner K, Kim Y. Semiautomatic 3-d prostate segmentation from TRUS images using spherical harmonics. IEEE Trans Med Imaging. 2006;25(12):1645–54.

    Google Scholar 

  131. Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging. 1999;18(10):897–908.

    Google Scholar 

  132. Vasantha M, Bharathi VS, Dhamodharan R. Medical image feature, extraction, selection and classification. Int J Eng Sci Technol. 2010;2(6):2071–6.

    Google Scholar 

  133. Wan C, Ye M, Yao C, Wu C. Brain MR image segmentation based on gaussian filtering and improved FCM clustering algorithm. In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2017; pp. 1–5.

  134. Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, Doel T, David AL, Deprest J, Ourselin S, et al. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans Med Imaging. 2018;37(7):1562–73.

    Google Scholar 

  135. Wittke C, Mayer J, Schweiggert F. On the classification of prostate carcinoma with methods from spatial statistics. IEEE Trans Inf Technol Biomed. 2007;11(4):406–14.

    Google Scholar 

  136. Wong KP. Medical image segmentation: methods and applications in functional imaging. Boston: Springer US; 2005. p. 111–82.

    Google Scholar 

  137. Worth AJ, Makris N, Caviness VS Jr, Kennedy DN. Neuroanatomical segmentation in MRI: technological objectives. Int J Pattern Recognit Artif Intell. 1997;11(08):1161–87.

    Google Scholar 

  138. Wu Z, Guo Y, Park SH, Gao Y, Dong P, Lee SW, Shen D. Robust brain ROI segmentation by deformation regression and deformable shape model. Med Image Anal. 2018;43:198–213.

    Google Scholar 

  139. Xing F, Xie Y, Su H, Liu F, Yang L. Deep learning in microscopy image analysis: a survey. IEEE Trans Neural Netw Learn Syst. 2017;29(10):4550–68.

    Google Scholar 

  140. Xu N, Ahuja N, Bansal R. Object segmentation using graph cuts based active contours. Comput Vis Image Underst. 2007;107(3):210–24.

    Google Scholar 

  141. Yan P, Xu S, Turkbey B, Kruecker J. Discrete deformable model guided by partial active shape model for TRUS image segmentation. IEEE Trans Biomed Eng. 2010;57(5):1158–66.

    Google Scholar 

  142. Yan Z, Tan C, Zhang S, Zhou Y, Belaroussi B, Yu HJ, Miller C, Metaxas DN. Automatic liver segmentation and hepatic fat fraction assessment in MRI. In: 2014 22nd International Conference on pattern recognition, 2014; pp. 3280–285.

  143. Yang F, Wan S, Chang Y. Improved method for gradient-threshold edge detector based on HVS. In: Computational Intelligence and Security. Springer Berlin Heidelberg, Berlin, Heidelberg; 2005, p. 1051–1056.

  144. Zanaty E. Improved region growing method for magnetic resonance images (MRIS) segmentation. Am J Remote Sens. 2013;1(2):53–60.

    Google Scholar 

  145. Zanaty E, Afifi A. A watershed approach for improving medical image segmentation. Comput Methods Biomech Biomed Eng. 2013;16(12):1262–72.

    Google Scholar 

  146. Zhang DQ, Chen SC. A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med. 2004;32(1):37–50.

    Google Scholar 

  147. Zhang R, Zhao L, Lou W, Abrigo JM, Mok VC, Chu WC, Wang D, Shi L. Automatic segmentation of acute ischemic stroke from DWI using 3-d fully convolutional densenets. IEEE Trans Med Imaging. 2018;37(9):2149–60.

    Google Scholar 

  148. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging. 2001;20(1):45–57.

    Google Scholar 

  149. Zhang Z, Liu Q, Wang Y. Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett. 2018;15(5):749–53.

    Google Scholar 

  150. Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3d CT images for anatomical structure segmentation based on an FCN voting method. Med Phys. 2017;44(10):5221–33.

    Google Scholar 

  151. Zhou Z, Rahman Siddiquee MM, Tajbakhsh N, Liang J. Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer International Publishing, Cham; 2018, p. 3–11. http://link.springer.com/chapter/10.1007/978-3-030-00889-5_1.

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    Yahya Alzahrani & Boubakeur Boufama

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Alzahrani, Y., Boufama, B. Biomedical Image Segmentation: A Survey. SN COMPUT. SCI. 2, 310 (2021). https://doi.org/10.1007/s42979-021-00704-7

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