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

  • Abstract
  • Keywords
  • References
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  • 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.



  • Keywords

    Image Segmentation; Machine Learning; Deep Learning; Convolution Neural Network.

  • References

      [1] Dewangan Shailendra Kumar. "Importance & Applications of Digital 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 Research 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 labeling." 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 Letters, 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 automatic segmentation and identification of nasopharyngeal carcinoma." 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. "Segmentation 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 fusing 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 Segmentation using OTSU Thresholding Optimal Approach." Journal for Research, Vol.2 (5), pp-20-24, 2016.

      [10] Aja Fernández Santiago et al., "A local fuzzy thresholding methodology for multiregion image segmentation." Knowledge-Based Systems, 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 segmentation 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 Recognition, 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 neural network in non-texture image inpainting and restoration." 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 classification." 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 data 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 Technology, 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 classification." 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 segmentation." 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 approach for image segmentation based on SOM network." 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 hierarchical 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 segmentation approach." International Journal of System Assurance Engineering 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) algorithm 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 applications, 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 segmentation." 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 segmentation based on PCNN with adaptive threshold time constant." 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 Applications, 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 algorithm." 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 parameter-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 signals." Computers in Biology and Medicine, pp-1-9, 2017.

      [41] Zhu Songhao et al., "Deep neural network based image annotation." 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 convolutional 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 Medicine, 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 convolutional 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 convolutional 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 performance 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 imaging, 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 echocardiographic sequences based on dynamic convolutional neural networks." 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 Microscopy Images Improves 3-D Neuron Reconstruction." IEEE transactions 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 Automatic Characterization of Plaque Composition in Carotid Ultrasound." 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 Science, 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 Science, 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 statistical neural networks." IU-Journal of Electrical & Electronics Engineering, 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 algorithm on multiple embedded graphic processing units for brain magnetic resonance imaging segmentation." Computers & Electrical Engineering, 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 Applications, 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 myocardial 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 algorithms." 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 Computing, pp.1-10, 2017.

      [70] Küçükkülahlı Enver, Pakize Erdoğmuş and Kemal Polat. "Histogram-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 clustering algorithm for image segmentation." IEEE Transactions on Consumer 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 Processing, 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 Processing, 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 segmentation 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 expectation maximization algorithm by using B-splines functions: Application 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 atlases." NeuroImage, Vol.141, pp.542-555, 2016. https://doi.org/10.1016/j.neuroimage.2016.07.020.




Article ID: 19005
DOI: 10.14419/ijet.v7i4.19005

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