Abstract
Background
This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images.
Methods
T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter \(\gamma \) were used for classification. Then, the optimal SVM-based classifier, expressed as \((C,\gamma \)), was identified by performance evaluation (sensitivity, specificity and accuracy).
Results
Eight of 12 textural features were selected as principal features (eigenvalues \(\lambda _{\mathrm{c}}\ge 1\)). The 16 SVM-based classifiers were obtained using combination of (C, \(\gamma \)), and those with lower C and \(\gamma \) values showed higher performances, especially classifier of \((C = 1,\gamma = 0.083)\,(p<0.05\)). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of \((C = 1,\gamma = 0.083\)) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively.
Conclusion
The T1W images in SVM-based classifier \((C =1, \gamma = 0.083)\) at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.
Similar content being viewed by others
References
Bushby K, Finkel R, Birnkrant DJ, Case LE, Clemens PR, Cripe L, Kaul A, Kinnett K, McDonald C, Pandya S (2010) Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and pharmacological and psychosocial management. Lancet Neurol 9:77–93
Bowles DE, McPhee SW, Li C, Gray SJ, Samulski JJ, Camp AS, Li J, Wang B, Monahan PE, Rabinowitz JE (2012) Phase 1 gene therapy for Duchenne muscular dystrophy using a translational optimized AAV vector. Mol Ther 20:443–455
Hoffman EP, Brown RH, Kunkel LM (1987) Dystrophin: the protein product of the Duchenne muscular dystrophy locus. Cell 51:919–928
Flanigan KM, Dunn DM, von Niederhausern A, Soltanzadeh P, Howard MT, Sampson JB, Swoboda KJ, Bromberg MB, Mendell JR, Taylor LE (2011) Nonsense mutation—associated Becker muscular dystrophy: interplay between exon definition and splicing regulatory elements within the DMD gene. Hum Mutat 32:299–308
Popplewell LJ, Adkin C, Arechavala-Gomeza V, Aartsma-Rus A, de Winter CL, Wilton SD, Morgan JE, Muntoni F, Graham IR, Dickson G (2010) Comparative analysis of antisense oligonucleotide sequences targeting exon 53 of the human DMD gene: Implications for future clinical trials. Neuromuscul Disord 20:102–110
Popplewell LJ, Trollet C, Dickson G, Graham IR (2009) Design of phosphorodiamidate morpholino oligomers (PMOs) for the induction of exon skipping of the human DMD gene. Mol Ther 17:554–561
Gregorevic P, Allen JM, Minami E, Blankinship MJ, Haraguchi M, Meuse L, Finn E, Adams ME, Froehner SC, Murry CE (2006) rAAV6-microdystrophin preserves muscle function and extends lifespan in severely dystrophic mice. Nat Med 12:787–789
Wang B, Li J, Xiao X (2000) Adeno-associated virus vector carrying human minidystrophin genes effectively ameliorates muscular dystrophy in mdx mouse model. Proc Natl Acade Sci 97:13714–13719
Finanger EL, Russman B, Forbes SC, Rooney WD, Walter GA, Vandenborne K (2012) Use of skeletal muscle MRI in diagnosis and monitoring disease progression in Duchenne muscular dystrophy. Phys Med Rehabil Clin N Am 23:1–10
Giglio V, Pasceri V, Messano L, Mangiola F, Pasquini L, Russo AD, Damiani A, Mirabella M, Galluzzi G, Tonali P (2003) Ultrasound tissue characterization detectspreclinical myocardial structural changes inchildren affected by Duchenne muscular dystrophy. J Am Coll Cardiol 42:309–316
Wren TA, Bluml S, Tseng-Ong L, Gilsanz V (2008) Three-point technique of fat quantification of muscle tissue as a marker of disease progression in Duchenne muscular dystrophy: preliminary study. Am J Roentgenol 190:W8–W12
Rad AE, Amin IBM, Rahim MSM, Kolivand H (2015) Computer-aided dental caries detection system from X-ray images. Computational intelligence in information systems. Springer, Berlin, pp 233–243
Kim S-K, Park YJ, Toh K-A, Lee S (2010) SVM-based feature extraction for face recognition. Pattern Recognit 43:2871–2881
Saimurugan M, Ramachandran K, Sugumaran V, Sakthivel N (2011) Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine. Expert Syst Appl 38:3819–3826
Guo Z, Bai G (2009) Application of least squares support vector machine for regression to reliability analysis. Chin J Aeronaut 22:160–166
Behzad M, Asghari K, Eazi M, Palhang M (2009) Generalization performance of support vector machines and neural networks in runoff modeling. Expert Syst Appl 36:7624–7629
Davatzikos C, Resnick SM, Wu X, Parmpi P, Clark CM (2008) Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI. Neuroimage 41:1220–1227
Zhu X, Huang Z, Yang Y, Shen HT, Xu C, Luo J (2013) Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognit 46:215–229
Biswas K, Basu SK, editors (2011) Gesture recognition using Microsoft Kinect. In: IEEE 2011 5th international conference on automation, robotics and applications (ICARA), 2011
Rahman MM, Antani SK, Thoma GR (2011) A learning-based similarity fusion and filtering approach for biomedical image retrieval using SVM classification and relevance feedback. IEEE Trans Inf Technol Biomed 15:640–646
Omer R, Fu L, editors (2010) An automatic image recognition system for winter road surface condition classification. In: IEEE 2010 13th international IEEE conference on intelligent transportation systems (ITSC), 2010
Beom Choi S, Park JS, Chung JW, Yoo TK, Kim DW, editors (2014) Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine. In: 2014 36th annual international conference of the IEEE on engineering in medicine and biology society (EMBC), 2014
da Silva CA, Silva AC, Netto SMB, de Paiva AC, Junior GB, Nunes RA (2009) Lung nodules classification in ct images using Simpson’s index, geometrical measures and one-class svm. Machine learning and data mining in pattern recognition. Springer, Berlin, pp 810–822
Sohail ASM, Bhattacharya P, Mudur SP, Krishnamurthy S (2011) Classification of ultrasound medical images using distance based feature selection and fuzzy-SVM. Pattern recognition and image analysis. Springer, Berlin, pp 176–183
Li B, Meng M-H (2012) Tumor recognition in wireless capsule endoscopy images using textural features and SVM-based feature selection. IEEE Trans Inf Technol Biomed 16:323–329
Sela Y, Freiman M, Dery E, Edrei Y, Safadi R, Pappo O, Joskowicz L, Abramovitch R (2011) fMRI-based hierarchical SVM model for the classification and grading of liver fibrosis. IEEE Trans Biomed Eng 58:2574–2581
Fan Y, Resnick SM, Davatzikos C (eds) (2008) Feature selection and classification of multiparametric medical images using bagging and SVM. Medical imaging: international society for optics and photonics
Ren Y, Bai G (2010) Determination of optimal SVM parameters by using GA/PSO. J Comput 5:1160–1168
Chen J-L, Kundu A (1994) Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov model. IEEE Trans Pattern Anal Mach Intell 16:208–214
Chu B, Kampschulte A, Ferguson MS, Kerwin WS, Yarnykh VL, O’Brien KD, Polissar NL, Hatsukami TS, Yuan C (2004) Hemorrhage in the atherosclerotic carotid plaque: a high-resolution. MRI study Stroke. 35:1079–1084
Nixon M, Nixon MS, Aguado AS (2012) Feature extraction and image processing for computer vision. Academic Press, London
Zacharaki EI, Wang S, Chawla S, Soo Yoo D, Wolf R, Melhem ER, Davatzikos C (2009) Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 62:1609–1618
Bro R, Smilde AK (2014) Principal component analysis. Anal Methods 6:2812–2831
Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433–459
Selvaraj H, Selvi ST, Selvathi D, Gewali L (2007) Brain MRI slices classification using least squares support vector machine. Int J Intell Comput Med Sci Image Process 1:21–33
Rippa S (1999) An algorithm for selecting a good value for the parameter c in radial basis function interpolation. Adv Comput Math 11:193–210
Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A (2011) Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes 4:299
Tang Y, Zhang Y-Q, Chawla NV, Krasser S (2009) SVMs modeling for highly imbalanced classification. IEEE Trans Syst Man Cybern B Cybern 39:281–288
Heijnen LA, Maas M, Lahaye MJ, Lalji U, Lambregts DM, Martens MH, Riedl RG, Beets GL, Beets-Tan RG (2014) Value of gadofosveset-enhanced MRI and multiplanar reformatting for selecting good responders after chemoradiation for rectal cancer. Eur Radiol 24:1845–1852
Funding
This study was supported by Key research project of Shanghai municipal government for private universities (2012-SHNGE-01ZD); 2015 joint research project between IBM and universities: clinical medical data analysis and processing (D-2111-15-001).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Zhang, MH., Ma, JS., Shen, Y. et al. Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines. Int J CARS 11, 1755–1763 (2016). https://doi.org/10.1007/s11548-015-1312-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-015-1312-0