Techniques for Detection and Analysis of Tumours from Brain MRI Images: A Review
DOI:
https://doi.org/10.14738/jbemi.31.1696Keywords:
tumour segmentation, medical image analysis, feature extraction, classificationAbstract
Analysis of MRI images and extraction of brain tumours from MRI images are challenging tasks in medical image processing. Researchers have contributed in segmenting and analysing brain tumour by applying varieties of the techniques and different hybrid approaches, however, due to diversity in appearance of tumour from patient to patient and also due to different tumour types, it has still been a challenge to exactly and correctly identify tumours from brain MRI image. Analysing brain MR images manually for finding exact boundaries of tumour by physicians is very time consuming and challenging due to low contrast MRI image and similarities of intensities between brain tissues. Many semi-automated and automated approaches have been developed to analyse MRI images and to delineate desired regions, such as tissues and tumour, and analyse their properties. This paper presents a comprehensive review of the state of the art methods for analysis of MRI images and methods for detection tumour from it. The review focuses, specifically, on important phases of MRI image analysis like feature extraction, segmentation and classification techniques. The challenges while processing brain MRI images as well as merits and demerits of existing methods for tumour analysis have been discussed.
References
(1) Stefan Bauer, Roland Wiest, Lutz-P Nolte and Mauricio Reyes , “A survey of MRI-based medical image analysis for brain tumour studies”, Journal of PHYSICS IN MEDICINE AND BIOLOGY, June 2013
(2) Roy, Sudipta, et al. "A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain." arXiv preprint arXiv:1312.6150 (2013).
(3) Gordillo Nelly, Eduard Montseny and Pilar Sobrevilla. "State of the art survey on MRI brain tumor segmentation." Elsevier, Magnetic resonance imaging 31.8 (2013): 1426-1438.
(4) MRI Brain Datasets available on http://www2.imm.dtu.dk/projects/BRATS2012/
(5) J. Mikulka and E. Gescheidtov, “An Improved Segmentation of Brain Tumour, Edema and Necrosis”, Progress In Electromagnetics Research Symposium Proceedings, Taipei, March, 2013
(6) Balafar, M. A., Ramli A. R., Saripan M. I., & Mashohor S., "Review of brain MRI image segmentation methods."Artificial Intelligence Review 33.3 (2010): 261-274.
(7) Jason J. Corso, Eitan Sharon, Shishir Dube, Suzie El-Saden, Usha Sinha and Alan Yuille, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification." Medical Imaging, IEEE Transactions on 27.5 (2008): 629-640.
(8) Bing Nan Li , Chee Kong Chui , Stephen Chang , S.H. Ong,"Integrating spatial fuzzy clustering with level set methods for automated medical image segmentation." Computers in Biology and
Medicine 41.1 (2011): 1-10.
(9) Khotanlou, Hassan, Olivier Colliot, Jamal Atif and Isabelle Bloch. "3D brain tumor segmentation in MRI using fuzzy classification, symmetry analysis and spatially constrained deformable models." Elsevier, Fuzzy Sets and Systems 160, no. 10 (2009): 1457-1473.
(10) Rajendran A. and R. Dhanasekaran. "Fuzzy clustering and deformable model for tumor segmentation on MRI brain image: a combined approach." Elsevier, Procedia Engineering 30 (2012): 327-333.
(11) Neeraj Sharma, Amit K. Ray, Shiru Sharma, K. K. Shukla, Satyajit Pradhan, Lalit M. Aggarwal, “Segmentation and classification of medical images using texture-primitive features: Application of BAM-type artificial neural network”, Journal of Medical Physics, Jul-Sep; 33(3): 119–126,
(12) Nanthagopal A. Padma and R. Sukanesh. "Wavelet statistical texture features-based segmentation and classification of brain computed tomography images." IET image processing 7.1 (2013): 25-32.
(13) Steenwijk Martijn D., Petra JW Pouwels, Marita Daams, Jan Willem van Dalen, Matthan WA Caan, Edo Richard, Frederik Barkhof, and Hugo Vrenken. "Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)." Elsevier, NeuroImage: Clinical 3 (2013): 462-469.
(14) Demirhan Ayşe, and İnan Güler. "Combining stationary wavelet transform and self-organizing maps for brain MR image segmentation", Elsevier, Engineering Applications of Artificial Intelligence 24.2 (2011): 358-367.
(15) Balafar, M. A. "Gaussian mixture model based segmentation methods for brain MRI images." Springer, Artificial Intelligence Review 41, no. 3 (2014): 429-439.
(16) Greenspan Hayit, Amit Ruf and Jacob Goldberger. "Constrained Gaussian mixture model framework for automatic segmentation of MR brain images." IEEE Transactions on Medical Imaging, 25.9 (2006): 1233-1245.
(17) Shah Saurabh A. and Narendra C. Chauhan. "An Automated Approach for Segmentation of Brain MR Images using Gaussian Mixture Model based Hidden Markov Random Field with Expectation Maximization." Journal of Biomedical Engineering and Medical Imaging 2.4 (2015): 57.
(18) Yousefi Sahar, Reza Azmi, and Morteza Zahedi. "Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.", Elsevier, Medical image analysis 16, no. 4 (2012): 840-848.
(19) Menze B. H., Van Leemput K., Lashkari D., Weber M. A., Ayache N., & Golland, P., “A generative model for brain tumor segmentation in multi-modal images”, In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2010 (pp. 151-159),Springer Berlin Heidelberg.
(20) Sachdeva, Jainy, Vinod Kumar, Indra Gupta, Niranjan Khandelwal, and Chirag Kamal Ahuja. "A novel content-based active contour model for brain tumor segmentation." Magnetic resonance imaging 30, no. 5 (2012): 694-715.
(21) Li, Ning, Miaomiao Liu, and Youfu Li. "Image segmentation algorithm using watershed transform and level set method." IEEE International Conference on Acoustics, Speech and Signal Processing, 2007, ICASSP 2007, Vol. 1. IEEE, 2007
(22) N. Otsu, “A Threshold Selection Method from Gray Level Histograms”, IEEE Transactions on Systems, Man, and Cybernetics, SMC-9 (1979) 62- 66.
(23) Dvorak P., K. Bartusek,and W. G. Kropatsch. "Automated Segmentation of Brain Tumour Edema in FLAIR MRI Using Symmetry and Thresholding.", PIERS Proceedings, Stockholm, Sweden, Aug. 12-15,
(24) N Costa, Alceu Ferraz, Gabriel Humpire-Mamani, and Agma Juci Machado Traina. "An efficient algorithm for fractal analysis of textures." Graphics, Patterns and Images (SIBGRAPI), 2012 25th SIBGRAPI
Conference on IEEE, 2012.
(25) Razlighi, Qolamreza R. and Yaakov Stern. "Blob-like feature extraction and matching for brain MR images." Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2011.
(26) Islam Aminul, Syed Reza, and Khan M. Iftekharuddin. "Multifractal texture estimation for detection and segmentation of brain tumors." IEEE Transactions on Biomedical Engineering, 60, no. 11 (2013): 3204-3215.
(27) Zhiqiang Lao, Dinggang Shen, Dengfeng Liu, Abbas F. Jawad, Elias R. Melhem, Lenore J. Launer, R. Nick Bryan, Christos Davatzikos, “Computer-Assisted Segmentation of White Matter Lesions in 3D MR Images Using Support Vector Machine”, Academic Radiology, Vol 15, No 3, March 2008
