Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey
Brain Tumor Segmentation through Image Processing Methods: A Literature Survey
DOI:
https://doi.org/10.14738/jbemi.62.6725Keywords:
Image Analysis, Image Segmentation, Brain Tumor Detection Region Based, Supervised Learning, Unsupervised Learning, Clustering, Watershed Segmentation, Convolutional Neural Networks, SVM, K-Means Clustering, MRI, CT scanAbstract
Image segmentation is one of the most trending fields in the domain of digital image processing. For years, researchers have shown a remarkable progress in the field of Image Segmentation, precisely, for brain tumor extraction from various medical imaging modalities including X-Ray, Computed Tomography and most importantly, Magnetic Resonance Images (MRI). In these medical imaging modalities, accurate and reliable brain tumor segmentation is extremely imperative to perform safe diagnose, healthy treatment planning and consistent treatment outcome evaluation in order to understand and cure the complexities of chronic diseases such as Cancer. This paper presents various image processing techniques that are currently being used for brain tumor extraction from medical images. Though some great work has been done in this domain but none of the techniques has been widely accepted to be brought into practice in real time clinical analysis. The paper concludes with proposing some solutions that would aid in refining the results of the techniques which will lead to clinical acceptance of these computer aided methods.
References
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3. Pooja Thakur, et al. (2015). Brain Tumor Detection, Segmentation using watershed segmentation and Morphological operations. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE).
4. Gursangeet Kaur and Jyoti Rani (June 2016). MRI Brain Tumor Segmentation Methods- A Review. International Journal of Current Engineering and Technology.
5. N.S.Zulpe, S.S.Chowhan."Statical Approach For MRI Brain Tumor Quantification". International Journal of Computer Applications(0975-8887), vol.35-No.7, December 2011.
6. D. Martin, C. Fowlkes, D. Tal, and J. Malik, ―A database of human segmented natural images and its application to evaluating segmentationalgorithms and measuring ecological statistics,‖ in Proc. 8th Int. Conf. Computer Vision, Jul. 2001, vol. 2, pp. 416–423.
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8. H. Tang, E.X. Wu, Q.Y. Ma, D. Gallagher, G.M. Perera, and T. Zhuang, ―MRI brain image segmentation by multi-resolution edge detection and region selection,‖ Computerized Medical Imaging and Graphics, vol. 24, pp. 349–357, 2000.
9. Mohammad Shajib Khadem, “MRI Brain image segmentation using graph cuts”, Master of Science Thesis in Communication Engineering, Department of Signals and Systems, Chalmers University Of Technology, Goteborg, Sweden, 2010.
10. Smita Pradhan , “Development of Unsupervised Image Segmentation Schemes for Brain MRI using HMRF model”, Master Thesis at Department of EE, NIT, Rourkela, 25 Mar 2010, pp. 4-6.
11. Roopali R.Laddha et al, A Review on Brain Tumor Detection Using Segmentation And Threshold Operations. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (1) , 2014, 607-611.
12. T. Logeswari, M. Karnan, ―An improved implementation of brain tumor detection using segmentation based on soft computingǁ, Page(s): 006-014, Journal of Cancer Research and Experimental Oncology Vol. 2(1), March 2010.
13. T. Sathies Kumar., et al. (2017). Brain Tumor Detection Using SVM Classifier. IEEE 3rd International Conference on Sensing, Signal Processing and Security (ICSSS).
14. Vida Harati, Rasoul Khayati, Abdolreza Farzan, “Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images,”Elsevier ltd, 2011.
15. Gonalves, P. C.; Tavares, J. M. R.; Jorge, R. N.: Segmentation and simulation of objects represented in images using physical principles. Computer Modeling in Engineering - Sciences, vol. 32, no. 2, pp. 45–55(2008).
16. Orlando J. Tobias and Rui Seara,”Image Segmentation by Histogram Thresholding Using Fuzzy Sets,” IEEE transactions on Image Processing,Vol. 11,NO. 12,PP-1457-1465,DEC 2002.
17. F.kurugollu, “color image segmentation using histogram multithresholding and fusion,” Image and Vision Comuting,Vol. 19,pp915-928,2001.
18. Jichuan Shi , ―Adaptive local threshold with shape information and its application to object segmentationǁ, Page(s)1123 - 1128, Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference,1923 Dec. 2009.
19. Roopali R.Laddha, S.A.Ladhake (2014), A Review on Brain Tumor Detection Using Segmentation And Threshold Operations, International Journal of Computer Science and Information Technologies, Vol. 5 (1), 607-611.
20. Alyaa H. Ali, Kawther A.Khalaph, Ihssan S.Nema. Segmentation of brain tumour using Enhanced Thresholding Algorithm and Calculatethe area of the tumour. IOSR Journal of Research & Method in Education (IOSR-JRME) e-ISSN: 2320–7388,p-ISSN: 2320–737X Volume 4, Issue 1 Ver. II (Jan. 2014), PP 58-62.
21. Saif D. Salman and Ahmed A. Bahrani, “Segmentation of tumor tissue in gray medical images using watershed transformation method,” Intl. Journal of Advancements in Computing Technology,Vol. 2, No. 4,pp123-127,OCT 2010.
22. Gang Li , ―Improved watershed segmentation with optimal scale based on ordered dither halftone and mutual informationǁ, Page(s) 296 - 300, Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference ,9-11 July 2010.
23. Swe Zin Oo, Aung Soe Khaing (2014), Brain tumor detection and segmentation using watershed segmentation and morphological operation, International Journal of Research in Engineering and Technology, eISSN: 2319-1163 | pISSN: 2321-7308.
24. Roshan G. Selkar, Prof. M. N. Thakare, “Brain Tumor Detection And Segmentation By Using Thresholding and Watershed Algorithm”, IJAICT ,vol 1, 2014.
25. Patil, A. J., et al. (2005). Pachpande, Automatic Brain Tumor Detection Using K-Means, 13896–13903.
26. H. Digabel C. Lantuejoul "Iterative algorithms" Quantitative Analysis of Microstructures in Materials Sciences Biology and Medicine pp. 85-99 1977.
27. S. Beucher C. Lantuejoul "Use of watersheds in contour detection" 1979.
28. Amoda, N. & Kulkarni, R. K. Image segmentation and detection using watershed transform and region based image retrieval. Int. J. Emerg. Trends & Techno. Comp. Sci. 2, 89–94 (2013).
29. C. C Benson V. L Lajish Kumar Rajamani "Brain Tumor Extraction from MRI Brain Images Using Marker Based Watershed Algorithm" International Conference on Advances in Computing Communications and Informatics (ICACCI) pp. 318-323 2015.
30. Rivest, J., Beucher, S., Delhomme. J, ―Marker-controlled segmentation: an application to electrical borehole imaging‖, Journal of Electronic Imaging, April pp. 136–142, 1992.
31. A. Sankari and S. Vigneshwari. “Automatic tumor segmentation using convolutional neural networks”. (2017) Third International Conference on Science Technology Engineering & Management (ICONSTEM).
32. Wang Mengqiao,. et al. (September 2017). “The multimodal brain tumor image segmentation based on convolutional neural networks”. (ICCIA).
33. R. Lang, L. Zhao and K. Jia, "Brain tumor image segmentation based on convolution neural network," 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, 2016, pp. 1402-1406.
34. W. Mengqiao, Y. Jie, C. Yilei and W. Hao, "The multimodal brain tumor image segmentation based on convolutional neural networks," 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), Beijing, 2017, pp. 336-339.
35. Nguyen Thanh Thuy, Tran Son Hai, Le Hoang Thai, “Image Classification using Support Vector Machine and Artificial Neural Network”, Vietnam.
36. Swapnil R. Telrandhe,. et al. “Detection of brain tumor from MRI images by using segmentation & SVM”. 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave).
37. Benson. C. C., et al. “Brain Tumor Segmentation from MR Brain Images using Improved Fuzzy c-Means Clustering and Watershed Algorithm”. 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.
38. Suganya, R., & Shanthi, R. (2012). Fuzzy C- Means Algorithm - A Review, 2(11), 1–3.
39. Ali Salem Bin Samma and Rosalina Abdul Salam. “Adaptation of K-Means Algorithm for Image Segmentation”. World Academy of Science, Engineering and Technology 50 2009.
40. Said, Ashraf & Sayed, Fatma. (2017). Comparative Study of Segmentation Techniques for Detection of Tumors Based on MRI Brain Images. International Journal of Bioscience, Biochemistry and Bioinformatics. 8. 10.17706/ijbbb.2018.8.1.1.
41. Tanuja, M., & Shewale, P. (2016). Detection of Brain Tumor Based On Segmentation Using Region Growing Method, 5(2), 173–176
42. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1).
43. Kumar, Arun. “A Novel Approach for Brain Tumor Detection Using Support Vector Machine, K-Means and PCA Algorithm.” (2017).
44. Gaurav Gupta, Vinay Singh, “Brain Tumor segmentation and classification using Fcm and support vector machine”. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 05, May-2017.
45. K Uma Suhasini, V. (December 2016). A Comparative Analysis of Brain Tumor Segmentation Techniques. Indian Journal of Science and Technology, Vol 9(48) .
46. V. Janani, P. M. (May 2013). Image Segmentation For Tumor Detection Using Fuzzy Inference System. International Journal of Computer Science and Mobile Computing, 244- 248.
47. Sundararaj GK, B. V. (2015). Robust classification of primary brain tumor in computer tomography images using K-NN and Linear SVM. . International Conference on Contemporary Computing and Informatics (IC3I).
48. Abdel- Maksoud E, E. M.-A. (March, 2015). Brain tumor segmentation using hybrid based clustering techniques. . Egyptian Informatics Journal., 71–81.
49. Ananda RS, T. T. (May, 2013). Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques. 5th International Conference on Biomedical Engineering and Informatics (BMEI).
50. Corso JJ, S. E.-S. (May, 2008). Efficient multilevel brain tumor segmentation with Integrated Bayesian model classification. IEEE Transactions on Medical Imaging., 629–40.
51. G. Rao, B. S. (2018). “Unsupervised learning algorithms for MRI Brain Tumor Segmentation”. SPACES. Vijayawada, India: IEEE.
52. J. Vijay and J. Subhashini, “An Efficient Brain Tumor Detection Methodology Using K-Means Clustering Algorithm”, International conference on Communication and Signal Processing, April 3-5, 2013, India.
53. Liu, Ziwei, et al. "Semantic image segmentation via deep parsing network." Proceedings of the IEEE International Conference on Computer Vision. 2015.
54. Zheng, Shuai, et al. "Conditional random fields as recurrent neural networks." Proceedings of the IEEE international conference on computer vision. 2015.
55. Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
56. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
57. Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
58. Pereira, Sérgio, et al. "Brain tumor segmentation using convolutional neural networks in MRI images." IEEE transactions on medical imaging 35.5 (2016): 1240-1251.
59. Havaei, Mohammad, et al. "Brain tumor segmentation with deep neural networks." Medical image analysis 35 (2017): 18-31.
60. Dvorak, Pavel, and Bjoern Menze. "Structured prediction with convolutional neural networks for multimodal brain tumor segmentation." Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge (2015): 13-24.
61. Zikic, Darko, et al. "Segmentation of brain tumor tissues with convolutional neural networks." Proceedings MICCAI-BRATS(2014): 36-39.
62. Kamnitsas, Konstantinos, et al. "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation." Medical image analysis 36 (2017): 61-78.
63. Yi, Darvin, et al. "3-D convolutional neural networks for glioblastoma segmentation." arXiv preprint arXiv:1611.04534(2016).
64. Urban, Gregor, et al. "Multi-modal brain tumor segmentation using deep convolutional neural networks." MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winning contribution (2014): 31-35.
65. Zhao, Xiaomei, et al. "A deep learning model integrating FCNNs and CRFs for brain tumor segmentation." Medical image analysis 43 (2018): 98-111.
66. Pham, Thuy Xuan, Patrick Siarry, and Hamouche Oulhadj. "Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation." Applied Soft Computing 65 (2018): 230-242.
67. Kumar, Dhirendra, et al. "A modified intuitionistic fuzzy c-means clustering approach to segment human brain MRI image." Multimedia Tools and Applications (2018): 1-25.
68. ShanmugaPriya, S., and A. Valarmathi. "Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images." Design Automation for Embedded Systems (2018): 1-13.
69. Subudhi, Asit, Subhranshu Jena, and Sukanta Sabut. "Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI." Medical & biological engineering & computing 56.5 (2018): 795-807.
70. Subudhi, A., Jena, S. & Sabut, S., “Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI”, Med Biol Eng Comput (2018) 56: 795.
71. A. Kapil, S. Shukla, “Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review”.
72. A. Junejo, S. Memon, I. Memon, S. Talpur, “Brain Tumor Segmentation Using 3D Magnetic Resonance Imaging Scans”, IEEE Internation Conference on Advanced Research in Engineering Sciences, Dubai 2018.
2. Oelze, M.L,Zachary, J.F. , O'Brien, W.D., Jr., ―Differentiation of tumor types in vivo by scatterer property estimates and parametric images using ultrasound backscatter ― , on page(s) :1014 – 1017 Vol.1, 5-8 Oct. 2003 .
3. Pooja Thakur, et al. (2015). Brain Tumor Detection, Segmentation using watershed segmentation and Morphological operations. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE).
4. Gursangeet Kaur and Jyoti Rani (June 2016). MRI Brain Tumor Segmentation Methods- A Review. International Journal of Current Engineering and Technology.
5. N.S.Zulpe, S.S.Chowhan."Statical Approach For MRI Brain Tumor Quantification". International Journal of Computer Applications(0975-8887), vol.35-No.7, December 2011.
6. D. Martin, C. Fowlkes, D. Tal, and J. Malik, ―A database of human segmented natural images and its application to evaluating segmentationalgorithms and measuring ecological statistics,‖ in Proc. 8th Int. Conf. Computer Vision, Jul. 2001, vol. 2, pp. 416–423.
7. J. Freixenet, X. Munoz, D. Raba, J. Marti, and X. Cufi, ―Yet another survey on image segmentation: Region and boundary information integration,‖ in Proc. 7th Eur. Conf. Computer Vision Part III, Copenhagen,Denmark, May 2002, pp. 408–422, LNCS.
8. H. Tang, E.X. Wu, Q.Y. Ma, D. Gallagher, G.M. Perera, and T. Zhuang, ―MRI brain image segmentation by multi-resolution edge detection and region selection,‖ Computerized Medical Imaging and Graphics, vol. 24, pp. 349–357, 2000.
9. Mohammad Shajib Khadem, “MRI Brain image segmentation using graph cuts”, Master of Science Thesis in Communication Engineering, Department of Signals and Systems, Chalmers University Of Technology, Goteborg, Sweden, 2010.
10. Smita Pradhan , “Development of Unsupervised Image Segmentation Schemes for Brain MRI using HMRF model”, Master Thesis at Department of EE, NIT, Rourkela, 25 Mar 2010, pp. 4-6.
11. Roopali R.Laddha et al, A Review on Brain Tumor Detection Using Segmentation And Threshold Operations. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (1) , 2014, 607-611.
12. T. Logeswari, M. Karnan, ―An improved implementation of brain tumor detection using segmentation based on soft computingǁ, Page(s): 006-014, Journal of Cancer Research and Experimental Oncology Vol. 2(1), March 2010.
13. T. Sathies Kumar., et al. (2017). Brain Tumor Detection Using SVM Classifier. IEEE 3rd International Conference on Sensing, Signal Processing and Security (ICSSS).
14. Vida Harati, Rasoul Khayati, Abdolreza Farzan, “Fully automated tumor segmentation based on improved fuzzy connectedness algorithm in brain MR images,”Elsevier ltd, 2011.
15. Gonalves, P. C.; Tavares, J. M. R.; Jorge, R. N.: Segmentation and simulation of objects represented in images using physical principles. Computer Modeling in Engineering - Sciences, vol. 32, no. 2, pp. 45–55(2008).
16. Orlando J. Tobias and Rui Seara,”Image Segmentation by Histogram Thresholding Using Fuzzy Sets,” IEEE transactions on Image Processing,Vol. 11,NO. 12,PP-1457-1465,DEC 2002.
17. F.kurugollu, “color image segmentation using histogram multithresholding and fusion,” Image and Vision Comuting,Vol. 19,pp915-928,2001.
18. Jichuan Shi , ―Adaptive local threshold with shape information and its application to object segmentationǁ, Page(s)1123 - 1128, Robotics and Biomimetics (ROBIO), 2009 IEEE International Conference,1923 Dec. 2009.
19. Roopali R.Laddha, S.A.Ladhake (2014), A Review on Brain Tumor Detection Using Segmentation And Threshold Operations, International Journal of Computer Science and Information Technologies, Vol. 5 (1), 607-611.
20. Alyaa H. Ali, Kawther A.Khalaph, Ihssan S.Nema. Segmentation of brain tumour using Enhanced Thresholding Algorithm and Calculatethe area of the tumour. IOSR Journal of Research & Method in Education (IOSR-JRME) e-ISSN: 2320–7388,p-ISSN: 2320–737X Volume 4, Issue 1 Ver. II (Jan. 2014), PP 58-62.
21. Saif D. Salman and Ahmed A. Bahrani, “Segmentation of tumor tissue in gray medical images using watershed transformation method,” Intl. Journal of Advancements in Computing Technology,Vol. 2, No. 4,pp123-127,OCT 2010.
22. Gang Li , ―Improved watershed segmentation with optimal scale based on ordered dither halftone and mutual informationǁ, Page(s) 296 - 300, Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference ,9-11 July 2010.
23. Swe Zin Oo, Aung Soe Khaing (2014), Brain tumor detection and segmentation using watershed segmentation and morphological operation, International Journal of Research in Engineering and Technology, eISSN: 2319-1163 | pISSN: 2321-7308.
24. Roshan G. Selkar, Prof. M. N. Thakare, “Brain Tumor Detection And Segmentation By Using Thresholding and Watershed Algorithm”, IJAICT ,vol 1, 2014.
25. Patil, A. J., et al. (2005). Pachpande, Automatic Brain Tumor Detection Using K-Means, 13896–13903.
26. H. Digabel C. Lantuejoul "Iterative algorithms" Quantitative Analysis of Microstructures in Materials Sciences Biology and Medicine pp. 85-99 1977.
27. S. Beucher C. Lantuejoul "Use of watersheds in contour detection" 1979.
28. Amoda, N. & Kulkarni, R. K. Image segmentation and detection using watershed transform and region based image retrieval. Int. J. Emerg. Trends & Techno. Comp. Sci. 2, 89–94 (2013).
29. C. C Benson V. L Lajish Kumar Rajamani "Brain Tumor Extraction from MRI Brain Images Using Marker Based Watershed Algorithm" International Conference on Advances in Computing Communications and Informatics (ICACCI) pp. 318-323 2015.
30. Rivest, J., Beucher, S., Delhomme. J, ―Marker-controlled segmentation: an application to electrical borehole imaging‖, Journal of Electronic Imaging, April pp. 136–142, 1992.
31. A. Sankari and S. Vigneshwari. “Automatic tumor segmentation using convolutional neural networks”. (2017) Third International Conference on Science Technology Engineering & Management (ICONSTEM).
32. Wang Mengqiao,. et al. (September 2017). “The multimodal brain tumor image segmentation based on convolutional neural networks”. (ICCIA).
33. R. Lang, L. Zhao and K. Jia, "Brain tumor image segmentation based on convolution neural network," 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Datong, 2016, pp. 1402-1406.
34. W. Mengqiao, Y. Jie, C. Yilei and W. Hao, "The multimodal brain tumor image segmentation based on convolutional neural networks," 2017 2nd IEEE International Conference on Computational Intelligence and Applications (ICCIA), Beijing, 2017, pp. 336-339.
35. Nguyen Thanh Thuy, Tran Son Hai, Le Hoang Thai, “Image Classification using Support Vector Machine and Artificial Neural Network”, Vietnam.
36. Swapnil R. Telrandhe,. et al. “Detection of brain tumor from MRI images by using segmentation & SVM”. 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave).
37. Benson. C. C., et al. “Brain Tumor Segmentation from MR Brain Images using Improved Fuzzy c-Means Clustering and Watershed Algorithm”. 2016 Intl. Conference on Advances in Computing, Communications and Informatics (ICACCI), Sept. 21-24, 2016, Jaipur, India.
38. Suganya, R., & Shanthi, R. (2012). Fuzzy C- Means Algorithm - A Review, 2(11), 1–3.
39. Ali Salem Bin Samma and Rosalina Abdul Salam. “Adaptation of K-Means Algorithm for Image Segmentation”. World Academy of Science, Engineering and Technology 50 2009.
40. Said, Ashraf & Sayed, Fatma. (2017). Comparative Study of Segmentation Techniques for Detection of Tumors Based on MRI Brain Images. International Journal of Bioscience, Biochemistry and Bioinformatics. 8. 10.17706/ijbbb.2018.8.1.1.
41. Tanuja, M., & Shewale, P. (2016). Detection of Brain Tumor Based On Segmentation Using Region Growing Method, 5(2), 173–176
42. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1).
43. Kumar, Arun. “A Novel Approach for Brain Tumor Detection Using Support Vector Machine, K-Means and PCA Algorithm.” (2017).
44. Gaurav Gupta, Vinay Singh, “Brain Tumor segmentation and classification using Fcm and support vector machine”. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 05, May-2017.
45. K Uma Suhasini, V. (December 2016). A Comparative Analysis of Brain Tumor Segmentation Techniques. Indian Journal of Science and Technology, Vol 9(48) .
46. V. Janani, P. M. (May 2013). Image Segmentation For Tumor Detection Using Fuzzy Inference System. International Journal of Computer Science and Mobile Computing, 244- 248.
47. Sundararaj GK, B. V. (2015). Robust classification of primary brain tumor in computer tomography images using K-NN and Linear SVM. . International Conference on Contemporary Computing and Informatics (IC3I).
48. Abdel- Maksoud E, E. M.-A. (March, 2015). Brain tumor segmentation using hybrid based clustering techniques. . Egyptian Informatics Journal., 71–81.
49. Ananda RS, T. T. (May, 2013). Automatic segmentation framework for primary tumors from brain MRIs using morphological filtering techniques. 5th International Conference on Biomedical Engineering and Informatics (BMEI).
50. Corso JJ, S. E.-S. (May, 2008). Efficient multilevel brain tumor segmentation with Integrated Bayesian model classification. IEEE Transactions on Medical Imaging., 629–40.
51. G. Rao, B. S. (2018). “Unsupervised learning algorithms for MRI Brain Tumor Segmentation”. SPACES. Vijayawada, India: IEEE.
52. J. Vijay and J. Subhashini, “An Efficient Brain Tumor Detection Methodology Using K-Means Clustering Algorithm”, International conference on Communication and Signal Processing, April 3-5, 2013, India.
53. Liu, Ziwei, et al. "Semantic image segmentation via deep parsing network." Proceedings of the IEEE International Conference on Computer Vision. 2015.
54. Zheng, Shuai, et al. "Conditional random fields as recurrent neural networks." Proceedings of the IEEE international conference on computer vision. 2015.
55. Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
56. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
57. Girshick, Ross, et al. "Rich feature hierarchies for accurate object detection and semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
58. Pereira, Sérgio, et al. "Brain tumor segmentation using convolutional neural networks in MRI images." IEEE transactions on medical imaging 35.5 (2016): 1240-1251.
59. Havaei, Mohammad, et al. "Brain tumor segmentation with deep neural networks." Medical image analysis 35 (2017): 18-31.
60. Dvorak, Pavel, and Bjoern Menze. "Structured prediction with convolutional neural networks for multimodal brain tumor segmentation." Proceeding of the Multimodal Brain Tumor Image Segmentation Challenge (2015): 13-24.
61. Zikic, Darko, et al. "Segmentation of brain tumor tissues with convolutional neural networks." Proceedings MICCAI-BRATS(2014): 36-39.
62. Kamnitsas, Konstantinos, et al. "Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation." Medical image analysis 36 (2017): 61-78.
63. Yi, Darvin, et al. "3-D convolutional neural networks for glioblastoma segmentation." arXiv preprint arXiv:1611.04534(2016).
64. Urban, Gregor, et al. "Multi-modal brain tumor segmentation using deep convolutional neural networks." MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winning contribution (2014): 31-35.
65. Zhao, Xiaomei, et al. "A deep learning model integrating FCNNs and CRFs for brain tumor segmentation." Medical image analysis 43 (2018): 98-111.
66. Pham, Thuy Xuan, Patrick Siarry, and Hamouche Oulhadj. "Integrating fuzzy entropy clustering with an improved PSO for MRI brain image segmentation." Applied Soft Computing 65 (2018): 230-242.
67. Kumar, Dhirendra, et al. "A modified intuitionistic fuzzy c-means clustering approach to segment human brain MRI image." Multimedia Tools and Applications (2018): 1-25.
68. ShanmugaPriya, S., and A. Valarmathi. "Efficient fuzzy c-means based multilevel image segmentation for brain tumor detection in MR images." Design Automation for Embedded Systems (2018): 1-13.
69. Subudhi, Asit, Subhranshu Jena, and Sukanta Sabut. "Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI." Medical & biological engineering & computing 56.5 (2018): 795-807.
70. Subudhi, A., Jena, S. & Sabut, S., “Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI”, Med Biol Eng Comput (2018) 56: 795.
71. A. Kapil, S. Shukla, “Brain Tumour Extration From MR Images Using Segmentation Techniques: A Review”.
72. A. Junejo, S. Memon, I. Memon, S. Talpur, “Brain Tumor Segmentation Using 3D Magnetic Resonance Imaging Scans”, IEEE Internation Conference on Advanced Research in Engineering Sciences, Dubai 2018.
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Published
2019-07-03
How to Cite
Zawish, M., Siyal, A. A., Shahani, S. H., Junejo, A. Z., & Khalil, A. (2019). Brain Tumor Segmentation through Region-based, Supervised and Unsupervised Learning Methods: A Literature Survey: Brain Tumor Segmentation through Image Processing Methods: A Literature Survey. British Journal of Healthcare and Medical Research, 6(2), 08–26. https://doi.org/10.14738/jbemi.62.6725
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