Abstract
Medical imaging interpretation and analysis requires automatic and exact classification. Several methods have been proposed in the last few years. This paper presents the effect of classification accuracy through different pre-processing techniques in the existing method tested on different Kernel SVM. Occurrence of irregular discontinuities causing bias field effect and intensity variations while capturing MR images requires the pre-processing of images. Three different preprocessing techniques such as Anisotropic diffusion, Homomorphic and Alphatrimmed filters are applied to brain MR images. We first enhance the attributes of the MRI image using these filters individually and then segments the tumor region. The relevant features are extracted from tumor regions and trained in a classifier. For feature extraction Wavelet transform is used, followed by feature reduction by using principle component analysis (PCA). The reduced features are trained with Kernel Support Vector Machine (KSVM) and classifies the tumor in MRI image as malignant and benign. We validate the performance of our approach on a dataset through multiple iterations to calculate the average classification accuracy subject to different preprocessing techniques.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hemanth, D.J., Anitha, J., Naaji, A., Geman, O., Popescu, D.E.: A modified deep convolutional neural network for abnormal brain image classification. IEEE Access 7, 4275–4283 (2019)
Padmashree, S., Nagapadma, R.: Performance measure analysis between anisotropic diffusion filter and bilateral filter for post processing of fractal compressed medical images. Int. J. Comput. Appl. 123(12), 36–43 (2015)
Agrawal, P., Chourasia, V., Kapoor, R., Agrawal, S.: A Comprehensive study of the image enhancement techniques. Int. J. Adv. Found. Res. Comput. (IJAFRC) 1, 85–89 (2014)
Sharma, P., Singh, H.: Improvement of brain tumor feature based segmentation using decision based alpha trimmed global mean filter. Int. J. Comput. Appl. 121(21), 13–20 (2015)
Liu, J., Guo, L.: A new brain MRI image segmentation strategy based on k-means clustering and SVM. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 270–273. IEEE (2015)
Chaplot, S., Patnaik, L.M., Jagannathan, N.R.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed. Signal Process. Control 1(1), 86–92 (2006)
El-Dahshan, E.-S.A., Hosny, T., Salem, A.-B.M.: Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process. 20(2), 433–441 (2010)
Saritha, M., Joseph, K.P., Mathew, A.T.: Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn. Lett. 34(16), 2151–2156 (2013)
Nayak, D.R., Dash, R., Majhi, B.: Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177, 188–197 (2016)
Işın, A., Direkoğlu, C., Şah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput. Sci. 102, 317–324 (2016)
Anitha, V., Murugavalli, S.: Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput. Vision 10(1), 9–17 (2016)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993 (2015)
Shree, N.V., 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)
Unde, A.S., Premprakash, V.A. Sankaran, P.: A novel edge detection approach on active contour for tumor segmentation. In: 2012 Students Conference on Engineering and Systems (SCES), pp. 1–6. IEEE, March 2012
Sajjad, M., Khan, S., Muhammad, K., Wu, W., Ullah, A., Baik, S.W.: Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J. Comput. Sci. 30, 174–182 (2019)
Usman, K., Rajpoot, K.: Brain tumor classification from multi-modality MRI using wavelets and machine learning. Pattern Anal. Appl. 20(3), 871–881 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dixit, A., Nanda, A. (2019). MR Brain Image Tumor Classification via Kernel SVM with Different Preprocessing Techniques. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T., Kashyap, R. (eds) Advances in Computing and Data Sciences. ICACDS 2019. Communications in Computer and Information Science, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-13-9939-8_20
Download citation
DOI: https://doi.org/10.1007/978-981-13-9939-8_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9938-1
Online ISBN: 978-981-13-9939-8
eBook Packages: Computer ScienceComputer Science (R0)