Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique

Author(s): Jyotsna Dogra, Shruti Jain, Ashutosh Sharma, Rajiv Kumar and Meenakshi Sood*

Volume 13, Issue 3, 2020

Page: [362 - 369] Pages: 8

DOI: 10.2174/2213275912666181207152633

Price: $65

Abstract

Background: This research aims at the accurate selection of the seed points from the brain MRI image for the detection of the tumor region. Since, the conventional way of manual seed selection leads to inappropriate tumor extraction therefore, fuzzy clustering technique is employed for the accurate seed selection for performing the segmentation through graph cut method.

Methods: In the proposed method Fuzzy Kernel Seed Selection technique is used to define the complete brain MRI image into different groups of similar intensity. Among these groups the most accurate kernels are selected empirically that show highest resemblance with the tumor. The concept of fuzziness helps making the selection even at the boundary regions.

Results: The proposed Fuzzy kernel selection technique is applied on the BraTS dataset. Among the four modalities, the proposed technique is applied on Flair images. This dataset consists of Low Grade Glioma (LGG) and High Grade Glioma (HGG) tumor images. The experiment is conducted on more than 40 images and validated by evaluating the following performance metrics: 1. Disc Similarity Coefficient (DSC), 2. Jaccard Index (JI) and 3. Positive Predictive Value (PPV). The mean DSC and PPV values obtained for LGG images are 0.89 and 0.87 respectively; and for HGG images it is 0.92 and 0.90 respectively.

Conclusion: On comparing the proposed Fuzzy kernel selection graph cut technique approach with the existing techniques it is observed that the former provides an automatic accurate tumor detection. It is highly efficient and can provide a better performance for HGG and LGG tumor segmentation in clinical application.

Keywords: MRI, graph cut, fuzzy, seed selection, dice sensitivity coefficient, positive predicted value.

Graphical Abstract

[1]
M.E. Davis, "Glioblastoma: Overview of disease and treatment", Clin. J. Oncol. Nurs., vol. 20, p. S2, 2016.
[2]
M. Arikan, B. Fröhler, and T. Möller, "Semi-automatic brain tumor segmentation using support vector machines and interactive seed selection"Proc. MICCAI-BRATS Workshop, 2016 , pp. 1-3. 2016
[3]
E.A. Eisenhauer, P. Therasse, J. Bogaerts, L.H. Schwartz, D. Sargent, R. Ford, J. Dancey, S. Arbuck, S. Gwyther, M. Mooney, and L. Rubinstein, "New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)", Eur. J. Cancer, vol. 45, pp. 228-247, January 2009.
[4]
P.Y. Wen, D.R. Macdonald, D.A. Reardon, T.F. Cloughesy, A.G. Sorensen, E. Galanis, J. DeGroot, W. Wick, M.R. Gilbert, A.B. Lassman, and C. Tsien, "Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group", J. Clin. Oncol., vol. 28, pp. 1963-1972, April 2010.
[5]
J. Larsen, S.B. Wharton, F. McKevitt, C. Romanowski, C. Bridgewater, H. Zaki, and N. Hoggard, "‘Low grade glioma’: an update for radiologists", Br. J. Radiol., vol. 90, p. 20160600, February 2017.
[6]
J.J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A. Yuille, "Efficient multilevel brain tumor segmentation with integrated bayesian model classification", IEEE Trans. Med. Imaging, vol. 27, pp. 629-640, 2008.
[7]
B.N. Saha, N. Ray, R. Greiner, A. Murtha, and H. Zhang, "Quick detection of brain tumors and edemas: A bounding box method using symmetry", Comput. Med. Imaging Graph., vol. 36, pp. 95-107, 2012.
[8]
J. Dogra, M. Sood, S. Jain, and N. Parashar, "Segmentation of magnetic resonance images of brain using thresholding techniques", 4th IEEE International Conference on Signal Processing, Computing and Control (ISPCC), 2017
[9]
J. Dogra, "Improved methods for analyzing MRI brain images", Net. Biol., vol. 8, no. 1, 2018.
[10]
Z.M. Wang, Y.C. Soh, Q. Song, and K. Sim, "Adaptive spatial informationtheoretic clustering for image segmentation", Pattern Recognit., vol. 42, no. 9, pp. 2029-2044, 2009.
[11]
S.N. Kumar, A. Lenin Fred, and P. Sebastin Varghese, "Suspicious lesion segmentation on brain, mammograms and breast MR images using new optimized spatial feature based super-pixel fuzzy C-means clustering", J. Digit. Imaging, pp. 1-14, 2018.
[12]
N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek, "A possibilistic fuzzy C-meansclustering algorithm", IEEE Trans. Fuzzy Syst., vol. 13, no. 4, pp. 517-530, 2005.
[13]
W. Cai, S. Chen, and D. Zhang, "Fast and robust fuzzy c-means clusteringalgorithms incorporating local information for image segmentation", Pattern Recognit., vol. 40, no. 3, pp. 825-838, 2007.
[14]
S. Krinidis, and V. Chatzis, "A robust fuzzy local information c-means clustering algorithm", IEEE Trans. Image Process., vol. 19, no. Issue. 5, pp. 1328-1337, 2010.
[15]
"H, Le Capitaine, and C. Frelicot, A cluster-validity index combining anoverlap measure and a separation measure based on fuzzyaggregation operators", IEEE Trans. Fuzzy Syst., vol. 19, no. 3, pp. 580-588, 2011.
[16]
A.F. Frangi, D. Rueckert, J.A. Schnabel, and W.J. Niessen, "Automatic construction of multiple-object three-dimensional statistical shape models: Application to cardiac modeling", IEEE Trans. Med. Imaging, vol. 21, pp. 1151-1166, 2002.
[17]
H. Ling, S.K. Zhou, Y. Zheng, B. Georgescu, M. Suehling, and D. Comaniciu, "Hierarchical, learning-based automatic liver segmentation", Computer Vision and Pattern Recognition (CVPR) IEEE Conference, 2008 pp. 1-8
[18]
S. Seifert, A. Barbu, S.K. Zhou, D. Liu, J. Feulner, M. Huber, M. Suehling, A. Cavallaro, and D. Comaniciu, "Hierarchical parsing and semantic navigation of full body CT data", Med. Imaging 2009: Image Processing, 2009 p. 725902
[19]
J.A. Sethian, "Level set methods and fast marching methods", J. Comput. Info. Technol., vol. 11, pp. 1-2, 2003.
[20]
S.O.R. Fedkiw, and S. Osher, "Level set methods and dynamic implicit surfaces", Surfaces, vol. 44, p. 77, 2002.
[21]
J. Yang, and J.S. Duncan, "3D image segmentation of deformable objects with joint shape-intensity prior models using level sets", Med. Image Anal., vol. 8, pp. 285-294, 2004.
[22]
R. Malladi, J.A. Sethian, and B.C. Vemuri, "Shape modeling with front propagation: A level set approach", IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, pp. 158-175, 1995.
[23]
D. Cremers, S.J. Osher, and S. Soatto, "Kernel density estimation and intrinsic alignment for shape priors in level set segmentation", Int. J. Comput. Vis., vol. 69, pp. 335-351, 2006.
[24]
S. Pereira, A. Pinto, V. Alves, and C.A. Silva, "Brain tumor segmentation using convolutional neural networks in MRI images", IEEE Trans. Med. Imaging, vol. 35, pp. 1240-1251, 2016.
[25]
P. Liskowski, and K. Krawiec, "Segmenting retinal blood vessels with deep neural networks", IEEE Trans. Med. Imaging, vol. 35, pp. 2369-2380, 2016.
[26]
S.P.K. Karri, D. Chakraborty, and J. Chatterjee, "Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration", Biomed. Opt. Express, vol. 8, pp. 579-592, 2017.
[27]
Y. Boykov, and M-P. Jolly, "Interactive organ segmentation using graph cuts", International conference on medical image computing and computer-assisted intervention, 2000 pp. 276-286
[28]
C.T. Zahn, "Graph-theoretical methods for detecting and describing gestalt clusters", IEEE Trans. Comput., vol. 100, pp. 68-86, 1971.
[29]
S.H. Kwok, and A.G. Constantinides, "A fast recursive shortest spanning tree for image segmentation and edge detection", IEEE Trans. Image Process., vol. 6, pp. 328-332, 1997.
[30]
P.F. Felzenszwalb, and D.P. Huttenlocher, "Efficient graph-based image segmentation", Int. J. Comput. Vis., vol. 59, pp. 167-181, 2004.
[31]
Y. Xu, and E.C. Uberbacher, "2D image segmentation using minimum spanning trees", Image Vis. Comput., vol. 15, pp. 47-57, 1997.
[32]
A.X. Falcão, and J.K. Udupa, "A 3D generalization of user-steered live-wire segmentation", Med. Image Anal., vol. 4, pp. 389-402, 2000.
[33]
A.X. Falcão, J. Stolfi, and R. de Alencar Lotufo, "The image foresting transform: Theory, algorithms, and applications", IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, pp. 19-29, 2004.
[34]
R. Ardon, and L.D. Cohen, "Fast constrained surface extraction by minimal paths", Int. J. Comput. Vis., vol. 69, pp. 127-136, 2006.
[35]
L. Grady, "Minimal surfaces extend shortest path segmentation methods to 3D", IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, pp. 321-334, 2010.
[36]
M. Sonka, M.D. Winniford, and S.M. Collins, "Robust simultaneous detection of coronary borders in complex images", IEEE Trans. Med. Imaging, vol. 14, pp. 151-161, 1995.
[37]
Y.Y. Boykov, and M-P. Jolly, "IInteractive graph cuts for optimal boundary & region segmentation of objects in ND images", Computer Vision, ICCV 2001. Proceedings. 8th IEEE International Conference on, 2001 pp. 105-112
[38]
Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts", IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, pp. 1222-1239, 2001.
[39]
W. Ju, D. Xiang, B. Zhang, L. Wang, I. Kopriva, and X. Chen, "Random walk and graph cut for co-segmentation of lung tumor on PET-CT images", IEEE Trans. Image Process., vol. 24, pp. 5854-5867, 2015.
[40]
Y. Boykov, and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision", IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, pp. 1124-1137, 2004.
[41]
I. Njeh, L. Sallemi, I.B. Ayed, K. Chtourou, S. Lehericy, D. Galanaud, and A.B. Hamida, "3D multimodal MRI brain glioma tumor and edema segmentation: A graph cut distribution matching approach", Comput. Med. Imaging Graph., vol. 40, pp. 108-119, 2015.
[42]
S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, J.B. Freymann, K. Farahani, and C. Davatzikos, "Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features", Sci. Data, vol. 4, p. 170117, 2017.
[43]
B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, and L. Lanczi, "The multimodal brain tumor image segmentation benchmark (BRATS)", IEEE Trans. Med. Imaging, vol. 34, pp. 1993-2024, 2015.
[44]
D. Jyotsna, S. Jain, and M. Sood, "Segmentation of MR images using hybrid kmean-graph cut technique", Procedia Comput. Sci., vol. 132, pp. 775-784, 2018.
[45]
M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jodoin, and H. Larochelle, "Brain tumor segmentation with deep neural networks", Med. Image Anal., vol. 35, pp. 18-31, 2017.
[46]
D. Kwon, R.T. Shinohara, H. Akbari, and C. Davatzikos, "Combining generative models for multifocal glioma segmentation and registration", International Conference on Medical Image Computing and Computer-Assisted Intervention, 2014 pp. 763-770

Rights & Permissions Print Cite
Article Metrics
12
© 2024 Bentham Science Publishers | Privacy Policy