Abstract
Tumor is the uncontrollable growth of abnormal cells in the brain which can be screened using magnetic resonance imaging (MRI). But, MRI is prone to poor contrast and noise during acquisition. This might affect the visibility of the tumor in the image which makes contrast enhancement an essential part of MR image analysis for tumor detection. In this method, a disk-shaped flat structuring element is applied with morphological operators consisting of bottom-hat, dilation and erosion for the purpose of noise controlled enhancement of MRI tumors. The outcomes of the proposed method are validated by image fidelity assessment parameters like: contrast improvement index (CII) and peak signal-to-noise ratio (PSNR).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Types of Brain Tumors. https://www.abta.org
Alankrita, A.R., Shrivastava, A., Bhateja, V.: Contrast improvement of cerebral MRI features using combination of non-linear enhancement operator and morphological filter. In: Proceedings of IEEE International Conference on Network and Computational Intelligence (ICNCI), vol. 4, pp. 182–187, Zhengzhou, China (2011)
Bahadure, N.B., Ray, A.K., Thethi, H.P.: Comparative approach of MRI-based brain tumor segmentation and classification using genetic algorithm. Int. J. Digit. Imaging 1–13 (2018)
Somasundaram, K., Kalaiselvi, T.: Automatic brain extraction methods for T1 magnetic resonance images using region labeling and morphological operations. Comput. Biol. Med. 41(8), 716–725 (2011)
MRI Sequences (Overview). https://radiopaedia.org/articles/mri-sequences-overview
Akram, M.U., Usman, A.: Computer aided system for brain tumor detection and segmentation. In: IEEE International Conference on Computer Networks and Information Technology (ICCNIT), pp. 299–302 (2011)
Yeganeh, H., Ziaei, A., Rezaie, A.: A novel approach for contrast enhancement based on histogram equalization. In: IEEE International Conference on Computer and Communication Engineering (ICCCE), pp. 256–260 (2008)
Stark, J.A.: Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans. Image Process. 9(5), 889–896 (2000)
Lidong, H., Wei, Z., Jun, W., Zebin, S.: Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement. IET Image Process. 9(10), 908–915 (2015)
Moses, C., Prasad, P.M.K.: Image enhancement using stationary wavelet transform. Int. J. Comput. Math. Sci. (IJCMS) 6(9), 84–88 (2017)
Kharrat, A., Benamrane, N., Messaoud, M.B., Abid, M.: Detection of brain tumor in medical images. In: 3rd IEEE International Conference on Signals, Circuits and Systems (SCS), pp 1–6 (2009)
Hassanpour, H., Samadiani, N., Salehi, S.M.: Using morphological transforms to enhance the contrast of medical images. Egypt. J. Radiol. Nucl. Med. 46(2), 481–489 (2015)
Bhadauria AS et al.: Skull stripping of brain MRI using mathematical morphology. Smart Intelligent Computing and Applications, pp. 775–780. Springer, Singapore (2020)
Raj, A., Srivastava, A., Bhateja, V.: Computer aided detection of brain tumor in magnetic resonance images. Int. J. Eng. Technol. 3(5), 523–532 (2011)
Verma, R., Mehrotra, R., Bhateja, V.: A new morphological filtering algorithm for pre-processing of electrocardiographic signals. In: Proceedings of the Fourth International Conference on Signal and Image Processing (ICSIP), pp. 193–201. Springer, India (2013)
Tiwari, D.K., Bhateja, V., Anand, D., Srivastava, A., Omar, Z.: Combination of EEMD and morphological filtering for baseline wander correction in EMG signals. In: Proceedings of 2nd International Conference on Micro-Electronics, Electromagnetics and Telecommunications, pp. 365–373. Springer, Singapore (2018)
Bhateja, V., Urooj, S., Mehrotra, R., Verma, R., Lay-Ekuakille, A., Verma, V.D.: A composite wavelets and morphology approach for ECG noise filtering. In: International Conference on Pattern Recognition and Machine Intelligence, pp. 361–366. Springer, Heidelberg, Berlin (2013)
Bhateja, V., Devi, S.: A novel framework for edge detection of microcalcifications using a non-linear enhancement operator and morphological filter. In: IEEE 3rd International Conference on Electronics Computer Technology (ICECT), vol. 5, pp. 419–424 (2011)
Bhateja, V., Urooj, S., Verma, R., Mehrotra, R.: A novel approach for suppression of powerline interference and impulse noise in ECG signals. In: IEEE International Conference on Multimedia, Signal Processing and Communication Technologies, pp. 103–107 (2013)
Arya, A., Bhateja, V., Nigam, M., Bhadauria, A.S.: Enhancement of brain MR-T1/T2 images using mathematical morphology. In: 3rd International Conference on ICT for Sustainable Development, pp. 1–8, Panaji, Goa (2018)
Chaddad, A., Tanougast, C.: Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images. Brain Inf. 3(1), 53–61 (2016)
Verma, R., Mehrotra, R., Bhateja, V.: An integration of improved median and morphological filtering techniques for electrocardiogram signal processing. In: IEEE 3rd International Conference Advance Computing, pp. 1223–1228 (2013)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, Chapter 10, pp. 689–794. Pearson Education (2009)
Das, S.S., Mohan, A.: Medical image enhancement techniques by bottom-hat and median filtering. Int. J. Electron. Commun. Comput. Eng. 5(4), 347–351 (2014)
Bhateja, V., Nigam, M., Bhadauria, A.S., Arya, A., Zhang, E.Y.D.: Human visual system based optimized mathematical morphology approach for enhancement of brain MR images. J. Ambient Intell. Hum. Comput. 1–9 (2019)
The Whole Brain Atlas. http://www.med.harvard.edu/aanlib/home.html
The Internet Brain Segmentation Repository. https://www.nitrc.org/projects/ibsr/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nigam, M., Bhateja, V., Arya, A., Bhadauria, A.S. (2020). An Evaluation of Contrast Enhancement of Brain MR Images Using Morphological Filters. In: Bhateja, V., Satapathy, S., Satori, H. (eds) Embedded Systems and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 1076. Springer, Singapore. https://doi.org/10.1007/978-981-15-0947-6_54
Download citation
DOI: https://doi.org/10.1007/978-981-15-0947-6_54
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0946-9
Online ISBN: 978-981-15-0947-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)