Brain abnormality detection using template matching

• Autorzy: Praveen G. B. , Agrawal A. , Pareek S. , Prince A.

Identyfikatory

DOI

10.1515/bams-2018-0029

• Języki publikacji: EN

Abstrakty

EN

Magnetic resonance imaging (MRI) is a widely used imaging modality to evaluate brain disorders. MRI generates huge volumes of data, which consist of a sequence of scans taken at different instances of time. As the presence of brain disorders has to be evaluated on all magnetic resonance (MR) sequences, manual brain disorder detection becomes a tedious process and is prone to inter- and intra-rater errors. A technique for detecting abnormalities in brain MRI using template matching is proposed. Bias filed correction is performed on volumetric scans using N4ITK filter, followed by volumetric registration. Normalized cross-correlation template matching is used for image registration taking into account, the rotation and scaling operations. A template of abnormality is selected which is then matched in the volumetric scans, if found, the corresponding image is retrieved. Post-processing of the retrieved images is performed by the thresholding operation; the coordinates and area of the abnormality are reported. The experiments are carried out on the glioma dataset obtained from Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). Glioma dataset consisted of MR scans of 30 real glioma patients and 50 simulated glioma patients. NVIDIA Compute Unified Device Architecture framework is employed in this paper, and it is found that the detection speed using graphics processing unit is almost four times faster than using only central processing unit. The average Dice and Jaccard coefficients for a wide range of trials are found to be 0.91 and 0.83, respectively.

Słowa kluczowe

EN

abnormality detection; image registration; magnetic resonance imaging; MRI; template matching; thresholding

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2018
• Tom: Vol. 14, no. 4
• Strony: art. no. 20180029
• Opis fizyczny: Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Praveen G. B.

• Department of Electrical and Electronics Engineering, BITS Pilani, K.K. Birla Goa Campus, Goa, India

autor

Agrawal A.

• Department of Electrical and Electronics Engineering, BITS Pilani, K.K. Birla Goa Campus, Goa, India

autor

Pareek S.

• Department of Electrical and Electronics Engineering, BITS Pilani, K.K. Birla Goa Campus, Goa, India

autor

Prince A.

• Department of Electrical and Electronics Engineering, BITS Pilani, K.K. Birla Goa Campus, Goa, India

Bibliografia

• [1] About brain tumors: a primer for patients and caregivers. Chicago, IL: American Brain Tumor Association, 2012.
• [2] Lee JH. Meningiomas: diagnosis, treatment, and outcome. 2008.
• [3] Mamelak AN, Jacoby DB. Targeted delivery of antitumoral therapy to glioma and other malignancies with synthetic chlorotoxin (tm-601). Expert Opin Drug Delivery 2007;4:175-86.
• [4] Harris GJ, Barta PE, Peng LW, Lee S, Brettschneider PD, Shah A, et al. MR volume segmentation of gray matter and white matter using manual thresholding: dependence on image brightness. Am J Neuroradiol 1994;15:225-30.
• [5] Suzuki H, Toriwaki J-I. Automatic segmentation of head MRI images by knowledge guided thresholding. Comput MedImaging Graphics 1991;15:233-40.
• [6] Adams R, Bischof L. Seeded region growing. IEEE Transpattern Anal Machine Intelligence 1994;6:641-7.
• [7] Mittelhaeusser G, Kruggel F. Fast segmentation of brain magnetic resonance tomograms. Computer Vision, Virtual Reality and Robotics in Medicine, pp. 237-41. Springer, 1995.
• [8] Wong K-P. Medical image segmentation: methods and applications in functional imaging. In Handbook of Biomedical Image Analysis, pp. 111-82. Boston, MA: Springer, 2005.
• [9] Dam E, Loog M, Letteboer M. Integrating automatic and interactive brain tumor segmentation. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference, vol. 3, pp. 790-3. IEEE, 2004.
• [10] Bezdek JC. Pattern recognition with fuzzy objective function algorithms. New York, USA: Springer Sci Business Media, 2013.
• [11] Bishop CM. Pattern recognition. Machine Learning 2006;128:1-58.
• [12] Sezgin M, Sankur B. Survey over image thresholding techniques and quantitative performance evaluation. J Elect Imaging 2004;13:146-68.
• [13] Otsu N. A threshold selection method from gray-level histograms. Automatica 1975;11:23-7.
• [14] Tsai D-M, Chen Y-H. A fast histogram-clustering approach for multi-level thresholding. Pattern Recogn Lett 1992;13:245-52.
• [15] Tsai W-H. Moment-preserving thresolding: a new approach. Comput Vision, Graphics Image Process 1985;29:377-93.
• [16] Yen J-C, Chang F-J, Chang S. A new criterion for automatic multilevel thresholding. IEEE Trans Image Process 1995;4:370-8.
• [17] Wang S, Haralick RM. Automatic multithreshold selection. ComputVision Graphics Image Process 1984;25:46-67.
• [18] Jain R, Kasturi R, Schunck BG. Machine vision, Vol. 5, McGraw-Hill: New York, 1995.
• [19] Sonka M, Hlavac V, Boyle R. Image processing, analysis, and machine vision. Cengage Learning, 2014.
• [20] Mary Synthuja Jain Preetha M, Padma Suresh L, Bosco J. Image segmentation using seeded region growing. In Computing, Electronics and Electrical Technologies (ICCEET), 2012, International Conference, pp. 576-83. Poland: IEEE, 2012.
• [21] Clarke LP, Velthuizen RP, Phuphanich S, Schellenberg JD, Arrington JA, Silbiger MM. MRI: stability of three supervised segmentation techniques. Magn Reson Imaging 1993;11:95-106.
• [22] Havaei M, Jodoin P-M, Larochelle H. Efficient interactive brain tumor segmentation as within-brain knn classification. In Pattern Recognition (ICPR), 2014 22nd International Conference, pp. 556–61. Stockholm, Sweden: IEEE, 2014.
• [23] Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol 1996;49:1225-31.
• [24] Clarke LP. MR image segmentation using MLM and artificial neural nets. MedPhys 1991;8:673.
• [25] Ozkan M, Dawant BM, Maciunas RJ. Neural-network-based segmentation of multi-modal medical images: a comparative and prospective study. IEEE Trans MedImaging 993;12:534-44.
• [26] Huang G-B, Zhu Q-Y, Siew CK. Real-time learning capability of neural networks. IEEE Trans Neural Networks 2006;17:863-78.
• [27] Martn-Landrove M, Villalta R. Brain tumor image segmentation using neural networks. In Proceedings of International Society of Magnetic Resonance in Medicine, p. 14, 2006.
• [28] Sauwen N, Acou M, Van Cauter S, Sima DM, Veraart J, Maes F, et al. Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. NeuroImage: Clin 2016;12:753-64.
• [29] Sammouda R, Niki N, Nishitani H. Neural networks based segmentation of magnetic resonance images. In Nuclear Science Symposium and Medical Imaging Conference, 1994, IEEE Conference Record, Vol 4, pp. 1827-31. New York: IEEE, 1994.
• [30] Yeh J-Y, Fu JC. A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI. Expert Syst Appl 2008;34:1285-95.
• [31] Praveen GB, Anita A. Hybrid approach for brain tumor detection and classification in magnetic resonance images. In Communication, Control and Intelligent Systems (CCIS), 2015, pp. 162-6. Mathura, India: IEEE, 2015.
• [32] Praveen GB, Anita A. Multi stage classification and segmentation of brain tumor. In Computing for Sustainable Global Development (INDIACom), 2016, 3rd International Conference, pp. 1628-32. IEEE, 2016.
• [33] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521:436.
• [34] Solanes A, Igual L, Radua J. Detection of midline brain abnormalities using convolutional neural networks. 2018.
• [35] Rezaei M, Yang H, Meinel C. Brain abnormality detection by deep convolutional neural network. arXiv preprint arXiv:1708.05206, 2017.
• [36] Pan Y, Huang W, Lin Z, Zhu W, Zhou J, Wong J, et al. Brain tumor grading based on neural networks and convolutional neural networks. In Engineering in Medicine and Biology Society (EMBC), 2015, 37th Annual International Conference of the IEEE, pp. 699-702, IEEE, 2015.
• [37] Rao V, Sarabi MS, Jaiswal A. Brain tumor segmentation with deep learning. MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS). 2015;56-9.
• [38] Zhao X, Wu Y, Song G, Li Z, Fan Y, Zhang Y. Brain tumor segmentation using a fully convolutional neural network with conditional random fields. In International Workshop on Brain Lesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, pp. 75-87. Cham, Switzerland: Springer, 2016.
• [39] Nie D, Zhang H, Adeli E, Liu L, Shen D. 3d deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 212-20. Cham, Switzerland: Springer, 2016.
• [40] Chen L, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks. NeuroImage Clin 2017;15:633-43.
• [41] Kamnitsas K, Ledig C, Newcombe VF, Simpson JP, Kane AD, Menon DK, et al. Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. MedImage Anal 2017;36:61-78.
• [42] Maier O, Schröder C, Forkert ND, Martinetz T, Handels H. Classifiers for ischemic stroke lesion segmentation: a comparison study. PLoS One 2015;10:1-18.
• [43] Praveen GB, Agrawal A, Sundaram P, Sardesai S. Ischemic stroke lesion segmentation using stacked sparse autoencoder. Comput Biol Med 2018 8;99:38-52. DOI: 10.1016/j.compbiomed.2018.05.027.
• [44] Musoko V, Procházka A. Non-linear median filtering of biomedical images. Institute of Chemical Technology, Department of Computing and Control Engineering, 2009.
• [45] De Boor C. On calculating with b-splines. J Approx Theor 1972;6:50-62.
• [46] Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, et al. N4itk: improved n3 bias correction. IEEE TransMed Imaging 2010;29:1310-20.
• [47] Tustison N, Wintermark M, Durst C, Avants B, et al. Ants and arboles. In MICCAI BraTS Workshop, pp. 47-50. Nagoya, Japan: IEEE, 2013.
• [48] Cordier N, Menze B, Delingette H, Ayache N. Patch-based segmentation of brain tissues. In MICCAI challenge on multimodal brain tumor segmentation, pp. 6-17. Nagoya, Japan: IEEE, 2013.
• [49] Buendia P, Taylor T, Ryan M, John N. A grouping artificial immune network for segmentation of tumor images. In MICCAI challenge on multimodal brain tumor segmentation, pp. 1-5. Nagoya, Japan: IEEE, 2013.
• [50] Doyle S, Vasseur F, Dojat M, Forbes F. Fully automatic brain tumor segmentation from multiple MR sequences using hidden Markov fields and variational EM. In MICCAI challenge on multimodal brain tumor segmentation, pp. 18-22. Nagoya, Japan: IEEE, 2013.
• [51] Zhao L, Sarikaya D, Corso JJ. Automatic brain tumor segmentation with MRF on supervoxels. In MICCAI challenge on multimodal brain tumor segmentation, pp. 51-4. Nagoya, Japan: IEEE, 2013.
• [52] Meier R, Bauer S, Slotboom J, Wiest R, Reyes M. A hybrid model for multimodal brain tumor segmentation. In MICCAI challenge on multimodal brain tumor segmentation, pp. 31-7. Nagoya, Japan: IEEE, 2013.
• [53] Guo X, Schwartz L, Zhao B. Semi-automatic segmentation of multimodal brain tumor using active contours. In MICCAI challenge on multimodal brain tumor segmentation, pp. 27-30. Nagoya, Japan: IEEE, 2013.
• [54] Pereira S, Festa J, Mariz JA, Sousa N, Silva CA. Automatic brain tissue segmentation of multi-sequence MR images using random decision forests. In MICCAI challenge on multimodal brain segmentation, pp. 23-6. Nagoya, Japan: IEEE, 2013.
• [55] Gupta M, Prabhakar Rao BV, Rajagopalan V, Das A, Kesavadas C. Volumetric segmentation of brain tumor based on intensity features of multimodality magnetic resonance imaging. In Computer, Communication and Control (IC4), 2015 International Conference, pp. 1-6. Indore, India: IEEE, 2015.
• [56] Tang H, Lu H, Liu W, Tao X. Tumor segmentation from single contrast MR images of human brain. In Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium, pp. 46-9. IEEE, 2015.
• [57] Reza S, Iftekharuddin KM. Multi-class abnormal brain tissue segmentation using texture. In MICCAI challenge on multimodal brain segmentation, pp. 38-42. Nagoya, Japan: IEEE, 2013.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).