Login Register Cart 0
Generic placeholder image

Current Signal Transduction Therapy

Editor-in-Chief

ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

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

Segmentation of Brain Magnetic Resonance Images using Deep Learning Classification and Multi-modal Composition

Author(s): R. Kala and P. Deepa*

Volume 15, Issue 2, 2020

Page: [94 - 108] Pages: 15

DOI: 10.2174/1574362414666181220105908

Abstract

Background: Accurate detection of brain tumor and its severity is a challenging task in the medical field. So there is a need for developing brain tumor detecting algorithms and it is an emerging one for diagnosis, planning the treatment and outcome evaluation.

Materials and Methods: Brain tumor segmentation method using deep learning classification and multi-modal composition has been developed using the deep convolutional neural networks. The different modalities of MRI such as T1, flair, T1C and T2 are given as input for the proposed method. The MR images from the different modalities are used in proportion to the information contents in the particular modality. The weights for the different modalities are calculated blockwise and the standard deviation of the block is taken as a proxy for the information content of the block. Then the convolution is performed between the input image of the T1, flair, T1C and T2 MR images and corresponding to the weight of the T1, flair, T1C, and T2 images. The convolution is summed between the different modalities of the MR images and its corresponding weight of the different modalities of the MR images to obtain a new composite image which is given as an input image to the deep convolutional neural network. The deep convolutional neural network performs segmentation through the different layers of CNN and different filter operations are performed in each layer to obtain the enhanced classification and segmented spatial consistency results. The analysis of the proposed method shows that the discriminatory information from the different modalities is effectively combined to increase the overall accuracy of segmentation.

Results: The proposed deep convolutional neural network for brain tumor segmentation method has been analysed by using the Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). The complete, core and enhancing regions are validated with Dice Similarity Coefficient and Jaccard similarity index metric for the Challenge, Leaderboard, and Synthetic data set. To evaluate the classification rates, the metrics such as accuracy, precision, sensitivity, specificity, under-segmentation, incorrect segmentation and over segmentation also evaluated and compared with the existing methods. Experimental results exhibit a higher degree of precision in the segmentation compared to existing methods.

Conclusion: In this work, deep convolution neural network with different modalities of MR image are used to detect the brain tumor. The new input image was created by convoluting the input image of the different modalities and their weights. The weights are determined using the standard deviation of the block. Segmentation accuracy is high with efficient appearance and spatial consistency. The assessment of segmented images is completely evaluated by using well-established metrics. In future, the proposed method will be considered and evaluated with other databases and the segmentation accuracy results should be analysed with the presence of different kind of noises.

Keywords: Deep convolutional neural networks, brain tumor, classification, magnetic resonance image, segmentation, modalities.

Graphical Abstract

[1]
Hayat AD, Ahmed AA. A clustering fusion technique for MR brain tissue segmentation. Neurocomputing 2018; 275: 546-59.
[http://dx.doi.org/10.1016/j.neucom.2017.08.051]
[2]
Zhao X, Wu Y, Song G, Li Z, Zhang Y, Fan Y. A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med Image Anal 2018; 43: 98-111.
[http://dx.doi.org/10.1016/j.media.2017.10.002] [PMID: 29040911]
[3]
Wang J, Kong J, Lu Y, Qi M, Zhang B. A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph 2008; 32(8): 685-98.
[http://dx.doi.org/10.1016/j.compmedimag.2008.08.004] [PMID: 18818051]
[4]
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88.
[http://dx.doi.org/10.1016/j.media.2017.07.005] [PMID: 28778026]
[5]
Tavallali P, Yazdi M, Khosravi MR. Robust cascaded skin detector based on AdaBoost. Multimedia Tools Appl 2018.
[http://dx.doi.org/10.1007/s11042-018-6385-7]
[6]
Torbati N, Ayatollahi A, Kermani A. An efficient neural network based method for medical image segmentation. Comput Biol Med 2014; 44: 76-87.
[http://dx.doi.org/10.1016/j.compbiomed.2013.10.029] [PMID: 24377691]
[7]
Haozhe J, Yong X, Yang S, et al. Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging. Neurocomputing 2017; 275: 1358-69.
[8]
Khosravi MR, Yazdi M. A lossless data hiding scheme for medical images using a hybrid solution based on IBRW error histogram com-putation and quartered interpolation with greedy weights. Neural Comput Appl 2018; 30(7): 2017-28.
[http://dx.doi.org/10.1007/s00521-018-3489-y]
[9]
Moeskops P, Viergever MA, Mendrik AM, de Vries LS, Benders MJ, Isgum I. Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans Med Imaging 2016; 35(5): 1252-61.
[http://dx.doi.org/10.1109/TMI.2016.2548501] [PMID: 27046893]
[10]
Tsai C, Manjunath BS, Jagadeesan R. Automated segmentation of brain MR images. Pattern Recognit 1995; 28(12): 1825-37.
[http://dx.doi.org/10.1016/0031-3203(95)00047-X]
[11]
Pereira S, Pinto A, Oliveira J, Mendrik AM, Correia JH, Silva CA. Automatic brain tissue segmentation in MR images using random forests and conditional random fields. J Neurosci Methods 2016; 270: 111-23.
[http://dx.doi.org/10.1016/j.jneumeth.2016.06.017] [PMID: 27329005]
[12]
Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control 2018; 39: 139-61.
[http://dx.doi.org/10.1016/j.bspc.2017.07.007]
[13]
Zhao X, Li P, Kohonen T. Contextual self-organizing map: software for constructing semantic representations. Behav Res Methods 2011; 43(1): 77-88. [Patter].
[http://dx.doi.org/10.3758/s13428-010-0042-z] [PMID: 21287105]
[14]
Lin KCR, Yang MS, Liu HC, Lirng JF, Wang PN. Generalized Kohonen’s competitive learning algorithms for ophthalmological MR image segmentation. Magn Reson Imaging 2003; 21(8): 863-70.
[http://dx.doi.org/10.1016/S0730-725X(03)00185-1] [PMID: 14599536]
[15]
Hung WL, Chen DH, Yang MS. Suppressed fuzzy-soft learning vector quantization for MRI segmentation. Artif Intell Med 2011; 52(1): 33-43.
[http://dx.doi.org/10.1016/j.artmed.2011.01.004] [PMID: 21435851]
[16]
Li C, Goldgof DB, Hall LO. Knowledge-based classification and tissue labeling of MR images of human brain. IEEE Trans Med Imaging 1993; 12(4): 740-50.
[http://dx.doi.org/10.1109/42.251125] [PMID: 18218469]
[17]
Matesin M, Loncaric S, Petravic D. A rule-base approach to stroke lesion analysis from CT brain images. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis (ISPA). 2001 June 19-21; Pula, Croatia. 219-3.
[18]
Chaplot S, Patnaik LM, Jagannathan N. Classification of magnetic resonance brain images using wavelets as input to support vector ma-chine and neural network. Biomed Signal Process Control 2006; 1(1): 86-92.
[http://dx.doi.org/10.1016/j.bspc.2006.05.002]
[19]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Proceedings of the conference on Advances in neural information processing systems (NIPS). 2012 Dec 03-08; Lake Tahoe. 1097-5.
[http://dx.doi.org/10.1145/3065386]
[20]
Girshick R, Donahue J, Darrell T, Malik J. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (CPVR). 2014 June 23-28; Columbus, Ohio. 580-7.
[http://dx.doi.org/10.1109/CVPR.2014.81]
[21]
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CPVR). 2015 June 8-10; Boston, Massachusetts. 3431-40.
[22]
Zikic D, Ioannou Y, Brown M, Criminisi A. Segmentation of brain tumor tissues with convolutional neural networks. Proceedings of the MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS). Boston, Massachusetts. 2014; pp. 36-9.
[23]
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal 2017; 35: 18-31.
[http://dx.doi.org/10.1016/j.media.2016.05.004] [PMID: 27310171]
[24]
Urban G, Bendszus M, Hamprecht F, Kleesiek J. Multi-modal brain tumor segmentation using deep convolutional neural networks. Proceedings of the MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS). Sep 04 2014; Boston, Massachusetts. Boston MICCAI 2014; pp. 31-5.
[25]
Pereira S, Pinto A, Alves V, Silva CA. Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI. In: Proceedings of the MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS) Aug 2015; Munich, Germany: Munich: MICCAI. 2015; pp. 52-5.
[26]
Milletari F, Ahmadi SA, Kroll C, et al. Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput Vis Image Underst 2017; 162: 92-102.
[http://dx.doi.org/10.1016/j.cviu.2017.04.002]
[27]
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging 2016; 35(5): 1240-51.
[http://dx.doi.org/10.1109/TMI.2016.2538465] [PMID: 26960222]
[28]
Farahani K, Menze B, Reyes M. Brain Tumor Segmentation (BraTS) Challenge 22014: Scope. Available from: http://www.braintumorsegmentation.org
[29]
Reza SMS, Iftekharuddin KM. Improved brain tumor tissue segmentation using texture features. Proceedings of the MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS). Boston, Massachusetts. 2014; pp. 27-30.
[30]
Meier R, Bauer S, Slotboom J, Wiest R, Reyes M. Appearance- and context-sensitive features for brain tumor segmentation. Proceedings of the MICCAI workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS). Boston, Massachusetts. 2014; pp. 20-6.
[31]
Tustison NJ, Avants BB, Cook PA, et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010; 29(6): 1310-20.
[http://dx.doi.org/10.1109/TMI.2010.2046908] [PMID: 20378467]
[32]
Gupta N, Khanna P, Bhatele P. Identification of gliomas from brain MRI through adaptive segmentation and run length of centralized patterns. J Comput Sci 2018; 25: 213-20.
[http://dx.doi.org/10.1016/j.jocs.2017.02.009]
[33]
Vishnuvarthanan G, Rajasekaran MP, Subbaraj P, Vishnuvarthanan A. An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput 2016; 38: 190-212.
[http://dx.doi.org/10.1016/j.asoc.2015.09.016]
[34]
Kala R, Deepa P. Adaptive hexagonal fuzzy hybrid filter for rician noise removal in MRI images. Neural Comput Appl 2017; 29(8): 237-49.
[http://dx.doi.org/10.1007/s00521-017-2953-4]
[35]
Dubey YK, Mushrif MM, Mitra K. Segmentation of brain MR images using rough set based intuitionistic fuzzy clustering. Biocybern Biomed Eng 2016; 36(2): 413-26.
[http://dx.doi.org/10.3758/s13428-010-0042-z] [PMID: 21287105]

Rights & Permissions Print Cite
Article Metrics
17
© 2024 Bentham Science Publishers | Privacy Policy