A survey on applications of machine learning techniques for medical image segmentation

  • Authors

    • Manaswini Jena Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
    • Smita Prava Mishra Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
    • Debahuti Mishra Siksha 'O' Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
    https://doi.org/10.14419/ijet.v7i4.19005

    Received date: September 5, 2018

    Accepted date: October 4, 2018

    Published date: November 15, 2018

  • Image Segmentation, Machine Learning, Deep Learning, Convolution Neural Network.

  • Abstract

    With development of science and technology in this digital era, the digital imaging is increasing expeditiously in every field. This digital image processing converts the image into its digital form to perform some operations on it to get either improved version of image or to get informational features from it. Different image processing techniques are available and image segmentation has a prime role in it. Segmentation of image is done principally to separate objects of interests from backgrounds. Ample of techniques are available for image segmentation. But, sometimes many techniques fail sporadically to yield the desired outcome. To fill up the requirement, the machine learning techniques come into play and perform well with satisfactory results. Here, is a fleeting review of machine learning techniques, mainly focusing on the artificial neural network with highlight of its improvements towards deep learning and convolution neural network along with some light on other machine learning techniques. It also includes brief descriptions of some neural networks used for segmenting different medical images and focus is given on convolution neural network which is developed primarily to work with images. The review will provide researchers a visualization and ideas to further use these techniques in improved ways for better performance for image segmentation.

  • References

    1. Dewangan Shailendra Kumar. "Importance & Applications of Digi-tal Image Processing." International Journal of Computer Science & Engineering Technology (IJCSET), Vol.7 (7), pp.316-320, 2016.
    2. Basavaprasad Bl and M. Ravi. "A study on the importance of image processing and its applications." IJRET:International Journal of Re-search in Engineering and Technology, Vol.3(3), pp.155-160, 2014.
    3. LinvZhonghua and Hongfei Yu. "The cell image segmentation and classification based on OTSU method and connected region label-ing." In Computer Science and Network Technology (ICCSNT), 2011 International Conference, Vol. 2, pp.1303-1306, 2011.
    4. Aganj Iman and Bruce Fischl, "Multimodal Image Registration through Simultaneous Segmentation." IEEE Signal Processing Let-ters, Vol.24 (11), pp.1661-1665, 2017. https://doi.org/10.1109/LSP.2017.2754263.
    5. Upadhyay Pankaj and Jitendra Kumar Chhabra. "Modified Self-Organizing Feature Map Neural Network (MSOFM NN) Based Gray Image Segmentation." Procedia Computer Science, Vol.54, pp.671-675, 2015. https://doi.org/10.1016/j.procs.2015.06.078.
    6. Mohammed Mazin Abed et al., "Artificial neural networks for au-tomatic segmentation and identification of nasopharyngeal carci-noma." Journal of Computational Science, Vol.21, pp.263-274, 2017. https://doi.org/10.1016/j.jocs.2017.03.026.
    7. Sethi Gaurav, Barjinder Singh Saini and Dilbag Singh. "Segmenta-tion of cancerous regions in liver using an edge-based and phase congruent region enhancement method." Computers & Electrical Engineering, Vol.53, pp.244-262, 2016. https://doi.org/10.1016/j.compeleceng.2015.06.025.
    8. Wu Kebin and David Zhang. "Robust tongue segmentation by fus-ing region-based and edge-based approaches." Expert Systems with Applications, Vol.42 (21), pp.8027-8038, 2015. https://doi.org/10.1016/j.eswa.2015.06.032.
    9. Vijay Patil Priyanka and N. C. Patil. "Gray Scale Image Segmenta-tion using OTSU Thresholding Optimal Approach." Journal for Re-search, Vol.2 (5), pp-20-24, 2016.
    10. Aja Fernández Santiago et al., "A local fuzzy thresholding method-ology for multiregion image segmentation." Knowledge-Based Sys-tems, Vol.83, pp.1-12, 2015. https://doi.org/10.1016/j.knosys.2015.02.029.
    11. Zaitoun Nida M. and Musbah J. Aqel. "Survey on image segmenta-tion techniques." Procedia Computer Science, Vol.65, pp.797-806, 2015. https://doi.org/10.1016/j.procs.2015.09.027.
    12. Niu Sijie et al., "Robust noise region-based active contour model via local similarity factor for image segmentation." Pattern Recogni-tion, Vol.61, pp.104-119, 2017. https://doi.org/10.1016/j.patcog.2016.07.022.
    13. Anjna Er, and Er Rajandeep Kaur. "Review of Image Segmentation Technique." International Journal, Vol.8 (4), pp-36-39, 2017.
    14. Saritha M., K. Paul Joseph and Abraham T. Mathew. "Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network", Pattern Recognition Letters, Vol.34 (16), pp.2151-2156, 2013. https://doi.org/10.1016/j.patrec.2013.08.017.
    15. Kuruvilla Jinsa and K. Gunavathi, "Lung cancer classification using neural networks for CT images." Computer methods and programs in biomedicine, Vol.113 (1), pp.202-209, 2014. https://doi.org/10.1016/j.cmpb.2013.10.011.
    16. Affonso Carlos, Renato Jose Sassi, and Ricardo Marques Barreiros. "Biological image classification using rough-fuzzy artificial neural network." Expert Systems with Applications, Vol.42 (24), pp.9482-9488, 2015. https://doi.org/10.1016/j.eswa.2015.07.075.
    17. Mohammed Mona Mahrous, Amr Badr, and M. B. Abdelhalim. "Image classification and retrieval using optimized pulse-coupled neural network." Expert systems with applications, Vol.42 (11), pp.4927-4936, 2015. https://doi.org/10.1016/j.eswa.2015.02.019.
    18. Alilou Vahid K. and Farzin Yaghmaee. "Application of GRNN neu-ral network in non-texture image inpainting and restora-tion." Pattern Recognition Letters, Vol.62, pp.24-31, 2015. https://doi.org/10.1016/j.patrec.2015.04.020.
    19. Mala K., V. Sadasivam and S. Alagappan. "Neural network based texture analysis of CT images for fatty and cirrhosis liver classifica-tion." Applied Soft Computing, Vol.32, pp.80-86, 2015. https://doi.org/10.1016/j.asoc.2015.02.034.
    20. Hiew Bee Yan, Shing Chiang Tan and Way Soong Lim. "Intra-specific competitive co-evolutionary artificial neural network for da-ta classification." Neurocomputing, Vol.185, pp.220-230, 2016. https://doi.org/10.1016/j.neucom.2015.12.051.
    21. Mitra Malay and R. K. Samanta. "Cardiac arrhythmia classification using neural networks with selected features." Procedia Technolo-gy, Vol.10, pp.76-84, 2013. https://doi.org/10.1016/j.protcy.2013.12.339.
    22. Hebboul Amel, Fella Hachouf, and Amel Boulemnadjel. "A new incremental neural network for simultaneous clustering and classifi-cation." Neurocomputing, Vol.169, pp.89-99, 2015. https://doi.org/10.1016/j.neucom.2015.02.084.
    23. Torbati Nima, Ahmad Ayatollahi, and Ali Kermani. "An efficient neural network based method for medical image segmenta-tion." Computers in biology and medicine, Vol.44, pp.76-87, 2014. https://doi.org/10.1016/j.compbiomed.2013.10.029.
    24. De Ailing and Chengan Guo "An adaptive vector quantization ap-proach for image segmentation based on SOM net-work." Neurocomputing, Vol.149, pp.48-58, 2015. https://doi.org/10.1016/j.neucom.2014.02.069.
    25. Ortiz A. et al., "MR brain image segmentation by growing hierar-chical SOM and probability clustering." Electronics Letters, Vol.47 (10), pp.585-586, 2011. https://doi.org/10.1049/el.2011.0322.
    26. Taneja Arti, Priya Ranjan, and Amit Ujlayan. "An efficient SOM and EM-based intravascular ultrasound blood vessel image segmen-tation approach." International Journal of System Assurance Engi-neering and Management, Vol.7 (4), pp.442-449, 2016. https://doi.org/10.1007/s13198-016-0482-7.
    27. Aghajari Ebrahim and Gharpure Damayanti Chandrashekhar. "Self-Organizing Map based Extended Fuzzy C-Means (SEEFC) algo-rithm for image segmentation." Applied Soft Computing, Vol.54, pp.347-363, 2017. https://doi.org/10.1016/j.asoc.2017.01.003.
    28. Khan Ahmad, M. Arfan Jaffar, and Tae-Sun Choi. "SOM and fuzzy based color image segmentation." Multimedia tools and applica-tions, Vol.64 (2), pp.331-344, 2013. https://doi.org/10.1007/s11042-012-1003-6.
    29. Jiang Yuan and Zhi-Hua Zhou. "SOM ensemble-based image seg-mentation." Neural Processing Letters, Vol.20 (3), pp.171-178, 2004. https://doi.org/10.1007/s11063-004-2022-8.
    30. Xu Xinzheng et al., "Pulse-coupled neural networks and parameter optimization methods." Neural Computing and Applications, pp.1-11, 2016.
    31. Wei Shuo, Qu Hong and Mengshu Hou. "Automatic image segmen-tation based on PCNN with adaptive threshold time con-stant." Neurocomputing, Vol.74 (9), pp.1485-1491, 2011. https://doi.org/10.1016/j.neucom.2011.01.005.
    32. Wang Zhaobin, Yide Ma and Jason Gu. "Multi-focus image fusion using PCNN." Pattern Recognition, Vol.43 (6), pp.2003-2016, 2010. https://doi.org/10.1016/j.patcog.2010.01.011.
    33. Zhou Dongguo et al., "Simplified parameters model of PCNN and its application to image segmentation." Pattern Analysis and Appli-cations, Vol.19 (4), pp.939-951, 2016. https://doi.org/10.1007/s10044-015-0462-6.
    34. Gao Chao, Dongguo Zhou and Yongcai Guo. "Automatic iterative algorithm for image segmentation using a modified pulse-coupled neural network." Neurocomputing, Vol.119, pp.332-338, 2013. https://doi.org/10.1016/j.neucom.2013.03.025.
    35. Yao Chang and Hou-jin Chen. "Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algo-rithm." Journal of Central South University of Technology, Vol.16 (4), pp.640-646, 2009. https://doi.org/10.1007/s11771-009-0106-3.
    36. Jiang Wen et.al. "Image segmentation with pulse-coupled neural network and canny operators." Computers & Electrical Engineering, Vol.46, pp.528-538, 2015. https://doi.org/10.1016/j.compeleceng.2015.03.028.
    37. Lian Jing et al., "An automatic segmentation method of a parame-ter-adaptive PCNN for medical images." International Journal of Computer Assisted Radiology and Surgery, pp.1-9, 2017.
    38. Chou Nigel et al., "Robust automatic rodent brain extraction using 3-D pulse-coupled neural networks (PCNN)." IEEE Transactions on Image Processing, Vol.20 (9), pp.2554-2564, 2011. https://doi.org/10.1109/TIP.2011.2126587.
    39. Guo, Ya nan et al., "Saliency Motivated Improved Simplified PCNN Model for object Segmentation." Neurocomputing, 2017.
    40. Acharya U. Rajendra et al., "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG sig-nals." Computers in Biology and Medicine, pp-1-9, 2017.
    41. Zhu Songhao et al., "Deep neural network based image annota-tion." Pattern Recognition Letters, Vol.65, pp.103-108, 2015. https://doi.org/10.1016/j.patrec.2015.07.037.
    42. Chandra B. and Rajesh K. Sharma. "Fast learning in deep neural networks", Neurocomputing, Vol.171, pp.1205-1215, 2016. https://doi.org/10.1016/j.neucom.2015.07.093.
    43. Qayyum Adnan et al., "Medical image retrieval using deep convolu-tional neural network." Neurocomputing, Vol.266, pp.8-20, 2017. https://doi.org/10.1016/j.neucom.2017.05.025.
    44. Acharya U. Rajendra et al., "A deep convolutional neural network model to classify heartbeats." Computers in Biology and Medi-cine, Vol.89, pp.389-396, 2017. https://doi.org/10.1016/j.compbiomed.2017.08.022
    45. Rasti Reza, Mohammad Teshnehlab and Son Lam Phung. "Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolu-tional neural networks." Pattern Recognition, vol.72, pp.381-390, 2017. https://doi.org/10.1016/j.patcog.2017.08.004.
    46. Long Jonathan, Evan Shelhamer and Trevor Darrell. "Fully convolu-tional networks for semantic segmentation." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440. 2015.
    47. Vinodhini G. and R. M. Chandrasekaran. "A comparative perfor-mance evaluation of neural network based approach for sentiment classification of online reviews." Journal of King Saud University-Computer and Information Sciences, Vol.28 (1), pp.2-12, 2016. https://doi.org/10.1016/j.jksuci.2014.03.024.
    48. Liskowski Paweł and Krzysztof Krawiec. "Segmenting Retinal Blood Vessels with Deep Neural Networks." IEEE transactions on medical imaging, Vol.35 (11), pp.2369-2380, 2016. https://doi.org/10.1109/TMI.2016.2546227.
    49. Kirbas Cemil and Francis Quek. "A review of vessel extraction techniques and algorithms." ACM Computing Surveys (CSUR), Vol.36 (2), pp.81-121, 2004. https://doi.org/10.1145/1031120.1031121
    50. Havaei Mohammad et al., "Brain tumor segmentation with deep neural networks." Medical image analysis, Vol.35, pp.18-31, 2017. https://doi.org/10.1016/j.media.2016.05.004.
    51. Pereira Sérgio et al., "Brain tumor segmentation using convolutional neural networks in MRI images." IEEE transactions on medical im-aging, Vol.35 (5), pp.1240-1251, 2016. https://doi.org/10.1109/TMI.2016.2538465.
    52. Yu Li et al., "Segmentation of fetal left ventricle in echocardio-graphic sequences based on dynamic convolutional neural net-works." IEEE Transactions on Biomedical Engineering, Vol.64 (8), pp.1886-1895, 2017. https://doi.org/10.1109/TBME.2016.2628401.
    53. Jiang Feng et al., "Medical image semantic segmentation based on deep learning." Neural Computing and Applications, pp.1-9, 2017.
    54. Li Rongjian et al., "Deep Learning Segmentation of Optical Micros-copy Images Improves 3-D Neuron Reconstruction." IEEE transac-tions on medical imaging, Vol.36 (7), pp.1533-1541, 2017. https://doi.org/10.1109/TMI.2017.2679713.
    55. Lekadir Karim et al., "A Convolutional Neural Network for Auto-matic Characterization of Plaque Composition in Carotid Ultra-sound." IEEE journal of biomedical and health informatics, Vol.21 (1), pp.48-55, 2017.
    56. Sadikoglu Fahreddin and Selin Uzelaltinbulat. "Biometric Retina Identification Based on Neural Network." Procedia Computer Sci-ence, Vol.102, pp.26-33, 2016. https://doi.org/10.1016/j.procs.2016.09.365.
    57. Tan Jen Hong et al., "Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network." Journal of Computational Science, Vol.20, pp.70-79, 2017.
    58. Zeng Zeng et al., "Multi-target deep neural networks: Theoretical analysis and implementation." Neurocomputing, pp.1-9, 2017.
    59. Meftah Boudjelal, Olivier Lezoray, and Abdelkader Benyettou. "Segmentation and edge detection based on spiking neural network model." Neural Processing Letters, Vol.32 (2), pp.131-146, 2010. https://doi.org/10.1007/s11063-010-9149-6.
    60. Land Walker H. et al., "PNN/GRNN ensemble processor design for early screening of breast cancer." Procedia Computer Sci-ence, Vol.12, pp.438-443, 2012. https://doi.org/10.1016/j.procs.2012.09.101.
    61. Kiyan Tüba and Tülay Yildirim. "Breast cancer diagnosis using sta-tistical neural networks." IU-Journal of Electrical & Electronics En-gineering, Vol.4 (2), pp.1149-1153, 2004.
    62. Song Tao et al., "A modified probabilistic neural network for partial volume segmentation in brain MR image." IEEE Transactions on Neural Networks, Vol.18 (5), pp.1424-1432, 2007. https://doi.org/10.1109/TNN.2007.891635.
    63. Hung Che-Lun and Yuan-Huai Wu. "Parallel genetic-based algo-rithm on multiple embedded graphic processing units for brain mag-netic resonance imaging segmentation." Computers & Electrical En-gineering, Vol.61, pp.373-383, 2017. https://doi.org/10.1016/j.compeleceng.2016.09.028.
    64. Moftah Hossam M. et al., "Adaptive k-means clustering algorithm for MR breast image segmentation." Neural Computing and Appli-cations, Vol.24 (7-8), pp.1917-1928, 2014. https://doi.org/10.1007/s00521-013-1437-4.
    65. Baracho Salety Ferreira et al., "A segmentation method for myocar-dial ischemia/infarction applicable in heart photos." Computers in Biology and Medicine, Vol.87, pp.285-301, 2017. https://doi.org/10.1016/j.compbiomed.2017.06.002.
    66. Balafar M. A. "Fuzzy C-mean based brain MRI segmentation algo-rithms." Artificial Intelligence Review, Vol.41 (3), pp.441-449, 2014. https://doi.org/10.1007/s10462-012-9318-2.
    67. Deng Wen-Qian et al., "A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction." Journal of Computer Science and Technology, Vol.31 (3), pp.501-511, 2016. https://doi.org/10.1007/s11390-016-1643-5.
    68. Zhang Xiaofeng et al., "Improved fuzzy clustering algorithm with non-local information for image segmentation." Multimedia Tools and Applications, Vol.76 (6), pp.7869-7895, 2017. https://doi.org/10.1007/s11042-016-3399-x.
    69. Pei Jialun et al., "Effective algorithm for determining the number of clusters and its application in image segmentation." Cluster Compu-ting, pp.1-10, 2017.
    70. Küçükkülahlı Enver, Pakize Erdoğmuş and Kemal Polat. "Histo-gram-based automatic segmentation of images." Neural Computing and Applications, Vol.27 (5), pp.1445-1450, 2016. https://doi.org/10.1007/s00521-016-2287-7.
    71. Siddiqui Fasahat Ullah and nor Ashidi Mat Isa. "Enhanced moving K-means (EMKM) algorithm for image segmentation." IEEE Transactions on Consumer Electronics, Vol.57 (2), pp.833-841, 2011. https://doi.org/10.1109/TCE.2011.5955230.
    72. Isa or Ashidi Mat et al., "Adaptive fuzzy moving K-means cluster-ing algorithm for image segmentation." IEEE Transactions on Con-sumer Electronics, Vol.55 (4), pp.2145-2153, 2009. https://doi.org/10.1109/TCE.2009.5373781.
    73. Xu Hongming and Mrinal Mandal. "Epidermis segmentation in skin histopathological images based on thickness measurement and k-means algorithm." EURASIP Journal on Image and Video Pro-cessing, 2015. https://doi.org/10.1186/s13640-015-0076-3.
    74. Namburu Anupama, Srinivas Kumar Samayamantula and Srinivasa Reddy Edara. "Generalised rough intuitionistic fuzzy c-means for magnetic resonance brain image segmentation." IET Image Pro-cessing, Vol.11 (9), pp.777-785, 2017. https://doi.org/10.1049/iet-ipr.2016.0891.
    75. Balafar Mohd Ali et al., "Review of brain MRI image segmentation methods." Artificial Intelligence Review, Vol.33 (3), pp.261-274, 2010. https://doi.org/10.1007/s10462-010-9155-0.
    76. Cabria Iván and Iker Gondra. "MRI segmentation fusion for brain tumor detection." Information Fusion, Vol.36, pp.1-9, 2017. https://doi.org/10.1016/j.inffus.2016.10.003.
    77. Zhao Bowen, Zhulou Cao and Sicheng Wang. "Lung vessel seg-mentation based on random forests." Electronics Letters, Vol.53 (4), pp.220-222, 2017. https://doi.org/10.1049/el.2016.4438.
    78. Hadrich Atizez, Mourad Zribi and Afif Masmoudi. "Bayesian ex-pectation maximization algorithm by using B-splines functions: Ap-plication in image segmentation." Mathematics and Computers in Simulation, Vol.120, pp.50-63, 2016. https://doi.org/10.1016/j.matcom.2015.06.007.
    79. Veredas Francisco, Héctor Mesa and Laura Morente. "Binary tissue classification on wound images with neural networks and bayesian classifiers." IEEE transactions on medical imaging, Vol.29 (2), pp.410-427, 2010. https://doi.org/10.1109/TMI.2009.2033595.
    80. Liu Yu-ting, Hong-xin Zhang, and Pei-hua Li, "Research on SVM-based MRI image segmentation." The Journal of China Universities of Posts and Telecommunications, Vol.18, pp.129-132, 2011. https://doi.org/10.1016/S1005-8885(10)60135-5.
    81. Lu Juan et al., "Automatic segmentation of scaling in 2-d psoriasis skin images." IEEE transactions on medical imaging, Vol.32 (4), pp.719-730, 2013. https://doi.org/10.1109/TMI.2012.2236349.
    82. Iglesias Juan Eugenion et al., "Bayesian longitudinal segmentation of hippocampal substructures in brain MRI using subject-specific at-lases." NeuroImage, Vol.141, pp.542-555, 2016. https://doi.org/10.1016/j.neuroimage.2016.07.020.

  • Downloads

  • How to Cite

    Jena, M., Prava Mishra, S., & Mishra, D. (2018). A survey on applications of machine learning techniques for medical image segmentation. International Journal of Engineering and Technology, 7(4), 4489-4495. https://doi.org/10.14419/ijet.v7i4.19005