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A Comparative Study of Medical Image Segmentation Methods for Tumor Detection

Authors: Mayssa Bensalah, Atef Boujelben, Mouna Baklouti, Mohamed Abid

Abstract:

Image segmentation has a fundamental role in analysis and interpretation for many applications. The automated segmentation of organs and tissues throughout the body using computed imaging has been rapidly increasing. Indeed, it represents one of the most important parts of clinical diagnostic tools. In this paper, we discuss a thorough literature review of recent methods of tumour segmentation from medical images which are briefly explained with the recent contribution of various researchers. This study was followed by comparing these methods in order to define new directions to develop and improve the performance of the segmentation of the tumour area from medical images.

Keywords: Features extraction, image segmentation, medical images, tumour detection.

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[1] Ben Rabeh A, Benzarti F, Amiri H. Segmentation of brain MRI using active contour model. Int J Imaging Syst Technol 2017;27(1):3–11.
[2] Anjali Wadhwa, Anuj Bhardwaj, Vivek Singh Verma, A review on brain tumor segmentation of MRI images, Magnetic Resonance Imaging 61 (2019) 247–259.
[3] Khalid N E A, Ibrahim S, Haniff P. MRI brain abnormalities segmentation using knearest neighbors (k-NN). Int J Comput Sci Eng 2011;3(2):980–90.
[4] Steenwijk M D, Pouwels P J, Daams M, van Dalen J W, Caan M W, Richard E. Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs). NeuroImage Clin 2013;3:462–9.
[5] Tustison N J, Shrinidhi K L, Wintermark M, Durst C R, Kandel B M, Gee J C. Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation (simplified) with ANTsR. Neuroinformatics 2015;13(2):209–25.
[6] Koley S, Sadhu A K, Mitra P, Chakraborty B, Chakraborty C. Delineation and diagnosis of brain tumors from post contrast T1-weighted MR images using rough granular computing and random forest. Appl Soft Comput 2016;41:453–65.
[7] Alexander Selvikvåg, Lundervold ,ArvidLundervold, An overview of deep learning in medical imaging focusing on MRI, Volume 29, Issue 2, May 2019, Pages 102-127
[8] Pereira S, Pinto A, Alves V, Silva C A. Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. 2015. p. 131–43.
[9] Pereira S, Pinto A, Alves V, Silva C A. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016;35(5):1240–51.
[10] Zhang W, Li R, Deng H, Wang L, Lin W, Ji S. Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 2015;108:214–24.
[11] Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M. Deep MRI brain extraction: a 3D convolutional neural network for skull stripping. NeuroImage 2016;129:460–9.
[12] Dong H, Yang G, Liu F, Mo Y, Guo Y. Automatic brain tumor detection and segmentation using u-net based fully convolutional networks. Annual Conf. Medical Image Understanding and Analysis. Springer; 2017. p. 506–17.
[13] Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y. Brain tumor segmentation with deep neural networks. Med Image Anal 2017;35:18–31.
[14] Zeynettin A , AlfiiaG , Assaf H , Daniel L Rubin , Bradley J Erickson, Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions, J Digit Imaging, 2017 Aug;30(4):449-459.
[15] Yang YX, Chong MS, Lim WS, et al. Validity of estimating muscle and fat volume from a single MRI section in older adults with sarcopenia and sarcopenic obesity. Clin Radiol 2017; 72(5). 427.e9-427.e14.
[16] Lee H, Troschel FM, Tajmir S, et al. Pixel-level deep segmentation: artificial intelligence quantifies muscle on computed tomography for body morphometric analysis. J Digit Imaging 2017; 30(4):487–498.
[17] Shih F Y, Cheng S. Automatic seeded region growing for color image segmentation. Image Vis Comput 2005;23(10):877–86.
[18] Wu M-N, Lin C-C, Chang C-C. Brain tumor detection using color-based K-means clustering segmentation. iih-msp. 2007. p. 245–50.
[19] Juang L-H, Wu M-N. MRI brain lesion image detection based on color-converted K-means clustering segmentation. Measurement 2010;43(7):941–9.
[20] Ahmed M M, Mohamad D B. Segmentation of brain MR images for tumor extraction by combining K-means clustering and Perona-Malik anisotropic diffusion model. Int J Image Process 2008;2(1):27–34.
[21] Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12(7):629–39.
[22] Jajuga K. L1-norm based fuzzy clustering. Fuzzy Sets Syst 1991;39(1):43–50.
[23] Hathaway R J, Bezdek J C, Hu Y. Generalized fuzzy c-means clustering strategies using Lp norm distances. Trans Fuzzy Syst 2000;8(5):576–82.
[24] Benaichouche A, Oulhadj H, Siarry P. Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and postsegmentation correction. Dig Signal Process 2013;23(5):1390–400.
[25] Chang H-H, Valentino D J. An electrostatic deformable model for medical image segmentation. Comput Med Imaging Graph 2008;32(1):22–35.
[26] Srikrishnan V, Chaudhuri S, Roy S D, Sevcovic D. On stabilisation of parametric active contours. IEEE Conf. Computer Vision and Pattern Recognition, CVPR’07. 2007. p. 1–6.
[27] Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9(1):62–6.
[28] Sujji G E, Lakshmi Y, Jiji G W. MRI brain image segmentation based on thresholding. Int J Adv Comput Res 2013;3(1):97.
[29] Sujan M, Alam N, Abdullah S, Jahirul M. A segmentation based automated system for brain tumor detection. Int J Comput Appl 2016;153(10):41–9.
[30] Ilhan U, Ilhan A. Brain tumor segmentation based on a new threshold approach. Proc Comput Sci 2017;120:580–7.
[31] Taheri S, Ong S, Chong V. Level-set segmentation of brain tumors using a threshold-based speed function. Image Vis Comput 2010;28(1):26–37.
[32] Adams R, Bischof L. Seeded region growing. IEEE Trans Pattern Anal Mach Intell 1994;16(6):641–7.
[33] Weglinski T, Fabijanska A. Brain tumor segmentation from MRI data sets using region growing approach. Proc. of 7th Int. Conf Perspective Technologies and Methods in MEMS Design (MEMSTECH). IEEE; 2011. p. 185–8.
[34] Bajwa I, Asghar M, Naeem M. Learning based improved seeded region growing algorithm for brain tumor identification. Proceedings of the Pakistan Academy of Sciences. vol. 54. 2017. p. 127–33.
[35] Abdel-Maksoud E, Elmogy M, Al-Awadi R. Brain tumor segmentation based on a hybrid clustering technique. Egypt Inf J 2015;16(1):71–81.
[36] Shenbagarajan A, Ramalingam V, Balasubramanian C, Palanivel S. Tumor diagnosis in MRI brain image using ACM segmentation and ANN-LM classification techniques. Indian J. Sci. Technol. 2016;9(1).
[37] Soltaninejad M, Zhang L, Lambrou T, Yang G, Allinson N, Ye X. MRI brain tumor segmentation and patient survival prediction using random forests and fully convolutional networks. Int. MICCAI Brainlesion Workshop. Springer; 2017. p. 204–15.
[38] Ma C, Luo G, Wang K. Concatenated and connected random forests with multiscale patch driven active contour model for automated brain tumor segmentation of MR images. IEEE Trans Med Imaging 2018;37(8):1943–54.
[39] Archana Chaudhari, Vaidarbhi Choudhari, Jayant Kulkarni, Automatic brain MR image tumor detection using Region Growing, International Journal of Industrial Electronics and Electrical Engineering, December 2017, ISSN(p): 2347-6982, ISSN(e): 2349-204X Volume-5, Issue-12.
[40] Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones T L, Barrick T R. Supervised learning based multimodal MRI brain tumor segmentation using texture features from supervoxels. Comput Methods Prog Biomed 2018;157:69–84
[41] Kistler et. al, The virtual skeleton database: an open access repository for biomedical research and collaboration. JMIR, 2013.