Top

Arabian Journal for Science and Engineering

Published in:

28-06-2019 | Research Article - Computer Engineering and Computer Science

Brain MR Imaging Tumor Detection Using Monogenic Signal Analysis-Based Invariant Texture Descriptors

Authors: Deepak O. Patil, Satish T. Hamde

Published in: Arabian Journal for Science and Engineering | Issue 11/2019

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Brain tumor is considered as a fatal disease with low survival rate and has the highest cost of care per patient. This article proposes a computer-assisted system for the recognition of brain tumor image through magnetic resonance imaging based on the monogenic signal analysis. From different monogenic components, textural descriptors are obtained using completed local binary pattern and gray-level co-occurrence matrix. In the pre-processing step, various filtering for noise removal and contrast enhancement techniques are implemented. Local phase, energy and orientation components originated from the monogenic signal analysis method are used for textural feature extraction. Fisher score-based filter approach for feature selection is then employed to derive the discriminating feature set. Finally, the acquired optimal feature set is classified using the support vector machine classifier. Two benchmark MR image datasets, e-health laboratory and Harvard medical laboratory, have been used to validate the system performance. Overall detection accuracy obtained was above 99%. The experimental results demonstrate the effectiveness of the proposed approach and the potential to assist the medical experts in enhancing the detection rate. Furthermore, the presented approach delivers superior performance in brain tumor image recognition as compared to existing techniques.

previous article Storage Node Allocation Methods for Erasure Code-based Cloud Storage Systems

next article Passage-Based Text Summarization for Legal Information Retrieval

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

National Brain Tumor Society-Quick brain tumor facts. http://www.braintumor.org (2018). Accessed 28 Aug 2018

Zhang, N.; Ruan, S.; Lebonvallet, S.; Liao, Q.; Zhu, Y.: Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst. 115(2), 256–269 (2011). https://doi.org/10.1016/j.cviu.2010.09.007 CrossRef

Rao, B.D.; Goswami, M.M.: Performance analysis of supervised and unsupervised techniques for brain tumor detection and segmentation from MR images. In: Kher, R., Gondaliya, D.N., Bhesaniya, M., Ladid, L., Atiquzzaman, M. (eds.) Proceedings of the International Conference on Intelligent Systems and Signal Processing, pp. 35–44. Springer, Singapore (2018)

Saman, S.; Jamjala Narayanan, S.: Survey on brain tumor segmentation and feature extraction of MR images. Int. J. Multimedia Inf. Retr. (2018). https://doi.org/10.1007/s13735-018-0162-2 CrossRef

Michael Mahesh, K.; Arokia Renjit, J.: Evolutionary intelligence for brain tumor recognition from MRI images: a critical study and review. Evol. Intell. 11(1), 19–30 (2018). https://doi.org/10.1007/s12065-018-0156-2 CrossRef

Sasikanth, S.; Suresh Kumar, S.: Glioma tumor detection in brain MRI image using ANFIS-based normalized graph cut approach. Int. J. Imaging Syst. Technol. 28(1), 64–71 (2017). https://doi.org/10.1002/ima.22257 CrossRef

Jayachandran, A.; Dhanasekaran, R.: Automatic detection of brain tumor in magnetic resonance images using multi-texton histogram and support vector machine. Int. J. Imaging Syst. Technol. 23(2), 97–103 (2012). https://doi.org/10.1002/ima.22041 CrossRef

Su, H.; Xing, F.; Yang, L.: Robust cell detection of histopathological brain tumor images using sparse reconstruction and adaptive dictionary selection. IEEE Trans. Med. Imaging 35(6), 1575–1586 (2016). https://doi.org/10.1109/TMI.2016.2520502 CrossRef

Chandra, G.R.; Rao, K.R.H.: Tumor detection in brain using genetic algorithm. Procedia Comput. Sci. 79, 449–457 (2016). https://doi.org/10.1016/j.procs.2016.03.058. In: Proceedings of International Conference on Communication, Computing and Virtualization (ICCCV) (2016)

10.

Rufus, H.A.; Selvathi, D.: Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier. Int. J. Imaging Syst. Technol. 28(2), 77–85 (2018). https://doi.org/10.1002/ima.22258 CrossRef

11.

Thirumurugan, P.; Shanthakumar, P.: Brain tumor detection and diagnosis using ANFIS classifier. Int. J. Imaging Syst. Technol. 26(2), 157–162 (2017). https://doi.org/10.1002/ima.22170 CrossRef

12.

Harikumar, R.; Karthick, G.; Kumar, B.V.: Earlier detection of cancer regions from MR image features and SVM classifiers. Int. J. Imaging Syst. Technol. 26(3), 196–208 (2016). https://doi.org/10.1002/ima.22177 CrossRef

13.

Si, T.; De, A.; Bhattacharjee, A.K.: Segmentation of brain MRI using wavelet transform and grammatical bee colony. J. Circuits Syst. Comput. 27(07), 1850108 (2018). https://doi.org/10.1142/S0218126618501086 CrossRef

14.

Sathish, P.; Elango, N.M.: Exponential cuckoo search algorithm to radial basis neural network for automatic classification in MRI images. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 7, 1–13 (2017). https://doi.org/10.1080/21681163.2017.1386593 CrossRef

15.

Bhuvaneswari, K.S.; Geetha, P.: Segmentation and classification of brain images using firefly and hybrid kernel-based support vector machine. J. Exp. Theor. Artif. Intell. 29(3), 663–678 (2017). https://doi.org/10.1080/0952813X.2016.1212106 CrossRef

16.

Mathew, A.R.; Anto, P.B.: Tumor detection and classification of MRI brain image using wavelet transform and SVM. In: International Conference on Signal Processing and Communication (ICSPC), pp. 75–78 (2017). https://doi.org/10.1109/CSPC.2017.8305810

17.

Zacharaki, E.I.; Wang, S.; Chawla, S.; Soo Yoo, D.; Wolf, R.; Melhem, E.R.; Davatzikos, C.: Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn. Reson. Med. 62(6), 1609–1618 (2009). https://doi.org/10.1002/mrm.22147 CrossRef

18.

Bahadure, N.B.; Ray, A.K.; Thethi, H.P.: Feature extraction and selection with optimization technique for brain tumor detection from MR images. In: International Conference on Computational Intelligence in Data Science (ICCIDS), pp. 1–7 (2017). https://doi.org/10.1109/ICCIDS.2017.8272635

19.

Raj, C.P.S.; Shreeja, R.: Automatic brain tumor tissue detection in T-1 weighted MRI. In: International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), pp. 1–4 (2017). https://doi.org/10.1109/ICIIECS.2017.8276094

20.

Subbanna, N.K.; Precup, D.; Collins, D.L.; Arbel, T.: Hierarchical probabilistic gabor and MRF segmentation of brain tumours in MRI volumes. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013, pp. 751–758. Springer, Berlin (2013)

21.

Panda, A.; Mishra, T.K.; Phaniharam, V.G.: Automated brain tumor detection using discriminative clustering based MRI segmentation. In: Tiwari, S., Trivedi, M.C., Mishra, K.K., Misra, A.K., Kumar, K.K. (eds.) Smart Innovations in Communication and Computational Sciences, pp. 117–126. Springer, Singapore (2019)CrossRef

22.

Sharma, M.; Purohit, G.N.; Mukherjee, S.: Information retrieves from brain MRI images for tumor detection using hybrid technique k-means and artificial neural network (KMANN). In: Perez, G.M., Mishra, K.K., Tiwari, S., Trivedi, M.C. (eds.) Networking Communication and Data Knowledge Engineering, pp. 145–157. Springer, Singapore (2018)CrossRef

23.

Suneetha, B.; Jhansi Rani, A.: Brain tumor detection in MR imaging using DW-MTM filter and region-growing segmentation approach. In: Bapi, R.S., Rao, K.S., Prasad, M.V.N.K. (eds.) First International Conference on Artificial Intelligence and Cognitive Computing, pp. 549–560. Springer, Singapore (2019)CrossRef

24.

Varuna Shree, N.; Kumar, T.N.R.: Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform. 5(1), 23–30 (2018). https://doi.org/10.1007/s40708-017-0075-5 CrossRef

25.

Bianchi, A.; Miller, J.V.; Tan, E.T.; Montillo, A.: Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests. In: IEEE 10th International Symposium on Biomedical Imaging, pp. 748–751 (2013). https://doi.org/10.1109/ISBI.2013.6556583

26.

Abbasi, S.; Tajeripour, F.: Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing 219, 526–535 (2017). https://doi.org/10.1016/j.neucom.2016.09.051 CrossRef

27.

Alam, M.; Vidyaratne, L.; Shboul, Z.; Pei, L.; Batchelder, T.; Iftekharuddin, K.M.: Deep learning and radiomics for glioblastoma survival prediction. In: Pre-conference Proceedings of the 7th MICCAI BraTS Challenge, pp. 11–18 (2018)

28.

Vidyaratne, L.; Alam, M.; Shboul, Z.; Iftekharuddin, K.M.: Deep learning and texture-based semantic label fusion for brain tumor segmentation. In: Proceedings of SPIE The International Society for Optical Engineering (2018). https://doi.org/10.1117/12.2292930

29.

Deutsch, E.; Limkin, E.J.; Schernberg, A.; Robert, C.; Sun, R.; Reuzé, S.; Ferté, C.; Dercle, L.; Zacharaki, E.I.; Paragios, N.: Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann. Oncol. 28(6), 1191–1206 (2017). https://doi.org/10.1093/annonc/mdx034 CrossRef

30.

Gilanie, G.; Bajwa, U.I.; Waraich, M.M.; Habib, Z.; Ullah, H.; Nasir, M.: Classification of normal and abnormal brain MRI slices using gabor texture and support vector machines. Signal Image Video Process. 12(3), 479–487 (2018). https://doi.org/10.1007/s11760-017-1182-8 CrossRef

31.

Manohar, L.; Ganesan, K.: Diagnosis of schizophrenia disorder in MR brain images using multi-objective BPSO based feature selection with fuzzy SVM. J. Med. Biol. Eng. 38(6), 917–932 (2018). https://doi.org/10.1007/s40846-017-0355-9 CrossRef

32.

Subudhi, A.; Jena, S.; Sabut, S.: Delineation of the ischemic stroke lesion based on watershed and relative fuzzy connectedness in brain MRI. Med. Biol. Eng. Comput. 56(5), 795–807 (2018). https://doi.org/10.1007/s11517-017-1726-7 CrossRef

33.

Wang, H.; Ahmed, S.N.; Mandal, M.: Computer-aided diagnosis of cavernous malformations in brain MR images. Comput. Med. Imaging Graph. 66, 115–123 (2018). https://doi.org/10.1016/j.compmedimag.2018.03.004 CrossRef

34.

eHealth Laboratory database. http://www.medinfo.cs.ucy.ac.cy/index.php/downloads/datasets (2018). Accessed 28 Aug 2018

35.

Harvard Medial School Database. http://www.med.harvard.edu/aanlib (2018). Accessed 28 Aug 2018

36.

Ramos-Llordén, G.; Vegas-Sánchez-Ferrero, G.; Martin-Fernandez, M.; Alberola-López, C.; Aja-Fernández, S.: Anisotropic diffusion filter with memory based on speckle statistics for ultrasound images. IEEE Trans. Image Process. 24(1), 345–358 (2015). https://doi.org/10.1109/TIP.2014.2371244 MathSciNetMATHCrossRef

37.

Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Heckbert, P.S. (ed.) Graphics Gems IV, pp. 474–485. Academic Press Professional Inc., Cambridge (1994)

38.

Felsberg, M.; Sommer, G.: The monogenic signal. IEEE Trans. Signal Process. 49(12), 3136–3144 (2001). https://doi.org/10.1109/78.969520 MathSciNetMATHCrossRef

39.

Dong, G.; Kuang, G.: Classification on the monogenic scale space: application to target recognition in SAR image. IEEE Trans. Image Process. 24(8), 2527–2539 (2015)MathSciNetMATHCrossRef

40.

Stein, E.M.: Singular Integrals and Differentiability Properties of Functions (PMS-30). Princeton University Press, Princeton (1970)

41.

Bridge, C.P.: Introduction to the monogenic signal. CoRR arXiv:1703.09199 (2017)

42.

Yang, M.; Zhang, L.; Shiu, S.C.; Zhang, D.: Monogenic binary coding: an efficient local feature extraction approach to face recognition. IEEE Trans. Inf. Forensics Secur. 7(6), 1738–1751 (2012)CrossRef

43.

Haralick, R.M.; Shanmugam, K.S.; Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRef

44.

Soh, L.; Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)CrossRef

45.

Clausi, D.A.: An analysis of co-occurrence texture statistics as a function of grey level quantization. Can. J. Remote Sens. 28, 45–62 (2002)CrossRef

46.

Guo, Z.; Zhang, L.; Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010). https://doi.org/10.1109/TIP.2010.2044957 MathSciNetMATHCrossRef

47.

Chang, C.C.; Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

48.

Birajdar, G.K.; Mankar, V.H.: Blind image forensics using reciprocal singular value curve based local statistical features. Multimed. Tools Appl. 77(11), 14153–14175 (2018). https://doi.org/10.1007/s11042-017-5021-2 CrossRef

49.

Cheng, J.; Yang, W.; Huang, M.; Huang, W.; Jiang, J.; Zhou, Y.; Yang, R.; Zhao, J.; Feng, Y.; Feng, Q.; Chen, W.: Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLOS ONE 11(6), 1–15 (2016). https://doi.org/10.1371/journal.pone.0157112 CrossRef

50.

Lauby-Secretan, B.; Scoccianti, C.; Loomis, D.; Benbrahim-Tallaa, L.; Bouvard, V.; Bianchini, F.; Straif, K.: Breast-cancer screening-viewpoint of the iarc working group. New Engl. J. Med. 372(24), 2353–2358 (2015). https://doi.org/10.1056/NEJMsr1504363 CrossRef

51.

Heath, M.; Bowyer, K.; Kopans, D.; Moore, R.; Kegelmeyer, W.P.: The digital database for screening mammography. In: Proceedings of the Fourth International Workshop on Digital Mammography (2000). https://doi.org/10.1007/978-94-011-5318-8_75

52.

Heath, M.; Bowyer, K.; Kopans, D.; Kegelmeyer, P.; Moore, R.; Chang, K.; Munishkumaran, S.: Current Status of the Digital Database for Screening Mammography, pp. 457–460. Springer, Dordrecht (1998). https://doi.org/10.1007/978-94-011-5318-8-75 CrossRef

53.

Kathirvel, R.; Batri, K.: Detection and diagnosis of meningioma brain tumor using ANFIS classifier. Int. J. Imaging Syst. Technol. 27(3), 187–192 (2017). https://doi.org/10.1002/ima.22222 CrossRef

Title: Brain MR Imaging Tumor Detection Using Monogenic Signal Analysis-Based Invariant Texture Descriptors
Authors: Deepak O. Patil
Satish T. Hamde
Publication date: 28-06-2019
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 11/2019
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-019-03989-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 11/2019

On Term Frequency Factor in Supervised Term Weighting Schemes for Text Classification

Embedded Fuzzy Logic Control System for Refrigerated Display Cabinets

A Novel Integrated Approach for Companion Vehicle Discovery Based on Frequent Itemset Mining on Spark

Storage Node Allocation Methods for Erasure Code-based Cloud Storage Systems

An Effective Mechanism for Selection of a Cloud Service Provider Using Cosine Maximization Method

An Optimal Codebook for Content-Based Image Retrieval in JPEG Compressed Domain

Premium Partners