Abstract
Brain diseases can cause invisible disorders, cognitive and behavioral changes. Their symptoms vary widely. In some cases, treatment can improve the symptoms while in other cases injuries become permanent. Many disorders are progressive. Therefore, early and accurate diagnosis of disorder is essential for improving disorder condition and patient’s quality of life. This paper presents the brain disease diagnosis system in which two feature extraction methods are compared. One of the feature extraction methods uses local binary pattern and steerable pyramid (SP) to decompose magnetic resonance (MR) brain images into subbands which are termed as LBPSP subbands. Another feature extraction method uses SP solely to decompose MR images into SP subbands. Energies over LBPSP and SP subbands are calculated. The features are subjected to backpropagation neural network classifier. To prove the effectiveness of the proposed system, multi-class disease classification is carried out on four MR image datasets. Also, ‘one-vs-all’ binary classification is performed on one of the datasets. Energy features of LBPSP subbands achieve multi-class classification accuracies of 97.67%, 97.27%, 94.67% and 85.01% on datasets DS-200, DS-310, DS-255 and DS-612, respectively. The performance measures of ‘one-vs-all’ binary class classification prove the competency and efficiency of LBPSP subband features over the existing methods.The comparative results of two feature extraction methods indicate that the energy features of LBPSP subbands have more discriminating potential than energy features of SP subbands. Experimental results reveal that energy features of LBPSP subbands lead to the existing classification methods.
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References
Sprawls Perry (2000) Magnetic resonance imaging: principles. Methods and techniques. Sprawls Education Foundation
Doi K (2007) Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31(4):198–211
Chaplot S, Patnaik LM, Jagannathan NR (2006) 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
El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441
Zhang Y, Dong Z, Wu L, Wang S (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053
Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of ripplet. Prog Electromagn Res 137:1–17
El-Dahshan ESA, Mohsen HM, Revett K, Salem ABM (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syst Appl 41(11):5526–5545
Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Feng C, Wang Q (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl 75:15601–15617
Shuihua W, Lu S, Dong Z, Yang J, Yang M, Zhang Y (2016) Dual-tree complex wavelet transform and twin support vector machine for pathological brain detection. Appl Sci 6(6):169
Wang S, Zhang Y, Dong Z, Du S, Ji G, Yan J, Yang J, Wang Q, Feng C, Phillips P (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164
Zhang Y, Dong Z, Wang S, Ji G, Yang J (2015) Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM). Entropy Multidiscip Digital Publ Inst 17(4):1795–1813
Wang S, Phillips P, Yang J, Sun P, Zhang Y (2016) Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients. Biomed Eng/Biomed Tech 61(4):431–441
Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177(Supplement C):188–197
Nayak DR, Dash R, Majhi B (2017) Stationary wavelet transform and AdaBoost with SVM based pathological brain detection in MRI scanning. CNS & Neurological Disorders-Drug Targets (Formerly Current Drug Targets-CNS & Neurological Disorders) 16(2):137–149
Zhang Y-D, Zhao G, Sun J, Wu X, Wang Z-H, Liu H-M, Govindaraj VV, Zhan T, Li J (2018) Smart pathological brain detection by synthetic minority oversampling technique, extreme learning machine, and Jaya algorithm. Multimed Tools Appl 77(17):22629–22648
Wang S, Du S, Atangana A, Liu A, Lu Z (2018) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 77(3):3701–3714
Nayak DR, Dash R, Majhi B (2018) Pathological brain detection using curvelet features and least squares SVM. Multimed Tools Appl 77(3):3833–3856
Nayak DR, Dash R, Majhi B (2018) Discrete ripplet-II transform and modified PSO based improved evolutionary extreme learning machine for pathological brain detection. Neurocomputing 282:232–247
Khalil M, Ayad H, Adib A (2018) Performance evaluation of feature extraction techniques in MR-Brain image classification system. Proc Comput Sci 127:218–225
Gudigar A, Raghavendra U, San TR, Ciaccio EJ, Acharya UR (2019) Application of multiresolution analysis for automated detection of brain abnormality using MR images: a comparative study. Future Gener Comput Syst 90:359–367
Nayak DR, Dash R, Chang X, Majhi B, Bakshi S (2018) Automated Diagnosis of Pathological Brain Using Fast Curvelet Entropy Features. IEEE Trans Sustain Comput
Harvard Medical School:Database: http://www.med.harvard.edu/AANLIB
Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recognit 29(1):51–59
Nanni L, Brahnam S, Lumini A (2011) Combining different local binary pattern variants to boost performance. Expert Syst Appl 38(5):6209–6216
Zhao Y, Jia W, Hu R-X, Min H (2013) Completed robust local binary pattern for texture classification. Neurocomputing 106:68–76
Simoncelli EP, Freeman WT (1995) The steerable pyramid: a flexible architecture for multi-scale derivative computation. In: Proceedings of International Conference on image processing, 1995, vol 3, pp 444–447
El Aroussi M, El Hassouni M, Ghouzali S, Rziza M (2009) Local steerable pyramid binary pattern sequence LSPBPS for face recognition method. Int J Signal Process 5:4
Muhammad G, Al-Hammadi MH, Hussain M, Bebis G (2014) Image forgery detection using steerable pyramid transform and local binary pattern. Machine Vis Appl 25(4):985–995
Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Machine Intell 11:674–693
Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall PTR, New York
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Hagan M, Demuth HB, Beale MH, De Jesús O (2014) Neural network design, 2nd edn, e-book
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Kale, V.V., Hamde, S.T. & Holambe, R.S. Brain disease diagnosis using local binary pattern and steerable pyramid. Int J Multimed Info Retr 8, 155–165 (2019). https://doi.org/10.1007/s13735-019-00174-x
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DOI: https://doi.org/10.1007/s13735-019-00174-x