Texture analysis method based on fractional Fourier entropy and fitness-scaling adaptive genetic algorithm for detecting left-sided and right-sided sensorineural hearing loss

Fundamenta Informaticae

Volumn 151, Issue 1-4, 2017, Pages 505-521

  Wang, Shuihua ^a,f   Yang, Ming ^b,g   Li, Jianwu ^c   Wu, Xueyan ^a,h   Wang, Hainan ^a,i   Liu, Bin ^d   Dong, Zhengchao ^e   Zhang, Yudong ^a,j  

^a NANJING NORMAL UNIVERSITY   (China)

^b NANJING MEDICAL UNIVERSITY   (China)

^c BEIJING INSTITUTE OF TECHNOLOGY   (China)

^d AFFILIATED ZHONGDA HOSPITAL OF SOUTHEAST UNIVERSITY   (China)

^e NEW YORK STATE PSYCHIATRIC INSTITUTE   (United States)

^f THE FIRST AFFILIATED HOSPITAL OF NANJING MEDICAL UNIVERSITY   (China)

^g ZHEJIANG UNIVERSITY   (China)

^h JILIN UNIVERSITY   (China)

ⁱ MANCHESTER METROPOLITAN UNIVERSITY   (United Kingdom)

^j Key Laboratory of Statistical Information Technology and Data Mining   (China)

more..

Author keywords

Fractional Fourier entropy; Genetic algorithm; Power rank fitness scaling; Sensorineural hearing loss; Texture analysis

Indexed keywords

BRAIN MAPPING; COMPUTER AIDED ANALYSIS; COMPUTER AIDED DIAGNOSIS; ENTROPY; FOURIER TRANSFORMS; GENETIC ALGORITHMS; HEALTH; MAGNETIC LEVITATION VEHICLES; MAGNETIC RESONANCE IMAGING; OPTIMIZATION;

ADAPTIVE GENETIC ALGORITHMS; COMPUTER AIDED DIAGNOSIS(CAD); FITNESS SCALING; FRACTIONAL FOURIER; MULTI LAYER PERCEPTRON; SENSORINEURAL HEARING LOSS; TEXTURE ANALYSIS; TEXTURE ANALYSIS METHOD;

AUDITION;

EID: 85015392790     PISSN: 01692968     EISSN: None     Source Type: Journal    
DOI: 10.3233/FI-2017-1507     Document Type: Article

References (50)

1. Ananthakrishnan S, et al. Human Frequency Following Response: Neural Representation of Envelope and Temporal Fine Structure in Listeners with Normal Hearing and Sensorineural Hearing Loss. Ear and Hearing, 2016;37(2):91-103. doi:10.1097/aud.0000000000000247.
2. Atangana A, et al. Partial Fractional Equations and Their Applications. Mathematical Problems in Engineering, 2015, Article ID: 387205, doi:10.1155/2015/387205.
3. Bilger M and Manning WG. Measuring Overfitting in Nonlinear Models: A new Method and an Application to Health Expenditures. Health Economics, 2015;24(1):75-85, doi:10.1002/hec.3003.
4. Bukhari SS, et al. Text-Line Extraction using a Convolution of Isotropic Gaussian Filter with a Set of Line Filters. in 11th International Conference on Document Analysis and Recognition. 2011. Los Alamitos: IEEE Computer Soc. pp. 579-583. doi:10.1109/icdar.2011.122, ISBN: 978-0-7695-4520-2.
5. Cattani C and Rao R. Tea Category Identification Using a Novel Fractional Fourier Entropy and Jaya Algorithm. Entropy, 2016;18(3), Article ID: 77. doi:10.3390/e18030077.
6. Chong DY, et al. Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features. Ieee Transactions on Medical Imaging, 2016;35(1):144-157. doi:10.1109/tmi.2015.2459064.
7. Duda D, et al. MRI Texture Analysis for Differentiation Between Healthy and Golden Retriever Muscular Dystrophy Dogs at Different Phases of Disease Evolution. in 14th IFIP TC 8 International Conference Computer Information Systems and Industrial Management (CISIM). 2015. Warsaw, POLAND: Springer Int Publishing Ag. pp. 255-266. doi:10.1007/978-3-319-24369-6 21, ISBN: 978-3-319-24369-6, 978-3-319-24368-9.
8. Ebtehaj I, and Bonakdari H. Bed Load Sediment Transport Estimation in a Clean Pipe using Multilayer Perceptron with Different Training Algorithms. KSCE Journal of Civil Engineering, 2016;20(2):581-589. doi:10.1007/s12205-015-0630-7.
9. Fan WL, et al. Altered contralateral auditory cortical morphology in unilateral sudden sensorineural hearing loss. Otology & Neurotology, 2015;36(10):1622-1627. doi:10.1097/mao.0000000000000892.
10. Florindo JB, et al. Enhancing texture descriptors by a neighborhood approach to the non-additive entropy. Digital Signal Processing, 2015;44:14-25. doi:10.1016/j.dsp.2015.05.004.
11. Gratsias G, et al. A quantitative evaluation of damage in normal appearing white matter in patients with multiple sclerosis using diffusion tensor MR imaging at 3 T. Acta Neurologica Belgica, 2015;115(2):111-116. doi:10.1007/s13760-014-0338-3.
12. Horvath A, et al. Increased diffusion in the normal appearing white matter of brain tumor patients: is this just tumor infiltration? Journal of Neuro-Oncology, 2016;127(1):83-90. doi:10.1007/s11060-015-2011-y.
13. Houssou N, and Zeller M. Operational Models for Improving the Targeting Efficiency of Development Policies: A Systematic Comparison of Different Estimation Methods Using Out-of-sample Tests. European Journal of Development Research, 2012;24(3):465-490. doi:10.1057/ejdr.2011.34.
14. Ji G. Fruit classification using computer vision and feedforward neural network. Journal of Food Engineering, 2014;143:167-177. doi:10.1016/j.jfoodeng.2014.07.001.
15. Karbowska-Chilinska J and Zabielski P. Genetic Algorithm with Path Relinking for the Orienteering Problem with Time Windows. Fundamenta Informaticae, 2014;135(4):419-431. doi:10.3233/fi-2014-1132.
16. Kim YJ, et al. EphA7 regulates spiral ganglion innervation of cochlear hair cells. Developmental Neurobiology, 2016;76(4):452-469. doi:10.1002/dneu.22326.
17. Komara M, et al. Clinical and molecular delineation of dysequilibrium syndrome type 2 and profound sensorineural hearing loss in an inbred Arab family. American Journal of Medical Genetics Part A, 2016;170(2):540-543. doi:10.1002/ajmg.a.37421.
18. Konig T, et al. Ultrasound texture-based CAD system for detecting neuromuscular diseases. International Journal of Computer Assisted Radiology and Surgery, 2015;10(9):1493-1503. doi:10.1007/s11548-014-1133-6.
19. Kwon K, et al. Multi-contrast MR image denoising for parallel imaging using multilayer perceptron. International Journal of Imaging Systems and Technology, 2016;26(1):65-75. doi:10.1002/ima.22158.
20. Li B, et al. Texture descriptions of lunar surface derived from LOLA data: Kilometer-scale roughness and entropy maps. Planetary and Space Science, 2015;117:303-311. doi:10.1016/j.pss.2015.07.004.
21. Li J. Detection of Left-Sided and Right-Sided Hearing Loss via Fractional Fourier Transform. Entropy, 2016;18(5), Article ID: 194, doi:10.3390/e18050194.
22. lo Storto C. Ecological Efficiency Based Ranking of Cities: A Combined DEA Cross-Efficiency and Shannon's Entropy Method. Sustainability, 2016;8(2):29. doi:10.3390/su8020124.
23. Ma ZS. Stochastic Populations, Power Law and Fitness Aggregation in Genetic Algorithms. Fundamenta Informaticae, 2013;122(3):173-206. doi:10.3233/fi-2013-787.
24. McNeish DM. Using Lasso for Predictor Selection and to Assuage Overfitting: A Method Long Overlooked in Behavioral Sciences. Multivariate Behavioral Research, 2015;50(5):13. doi:10.1080 /00273171.2015.1036965.
25. Pelich R, et al. Vessel Refocusing and Velocity Estimation on SAR Imagery Using the Fractional Fourier Transform. IEEE Transactions on Geoscience and Remote Sensing, 2016;54(3):1670-1684. doi:10.1109/tgrs.2015.2487378.
26. Peng B, et al. Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Scientific Reports, 2016;6, Article ID: 21816. doi:10.1038/srep21816.
27. Pham TD. The Kolmogorov-Sinai entropy in the setting of fuzzy sets for image texture analysis and classification. Pattern Recognition, 2016;53:229-237. doi:10.1016/j.patcog.2015.12.012.
28. Prabhu MV, et al. Modeling and optimization by response surface methodology and neural networkgenetic algorithm for decolorization of real textile dye effluent using Pleurotus ostreatus: a comparison study. Desalination and Water Treatment, 2016;57(28):13005-13019. doi:10.1080/19443994. 2015.1059372.
29. Sarbishei O, and Radecka K. Analysis of Mean-Square-Error (MSE) for Fixed-Point FFT Units. in International Symposium on Circuits and Systems. 2011. New York: IEEE. pp. 1732-1735, ISBN: 978-1-4244-9474-3.
30. Scheller C, et al. Prophylactic nimodipine treatment for cochlear and facial nerve preservation after vestibular schwannoma surgery: a randomized multicenter Phase III trial. Journal of Neurosurgery, 2016;124(3):657-664. doi:10.3171/2015.1.jns142001.
31. Smith AD, et al. Predicting Overall Survival in Patients With Metastatic Melanoma on Antiangiogenic Therapy and RECIST Stable Disease on Initial Posttherapy Images Using CT Texture Analysis. American Journal of Roentgenology, 2015;205(3):283-293. doi:10.2214/ajr.15.14315.
32. Smith SM. Fast robust automated brain extraction. Human Brain Mapping, 2002;17(3):143-155. doi:10.1002/hbm.10062.
33. Sorensen L, et al. Early detection of Alzheimer's disease using MRI hippocampal texture. Human Brain Mapping, 2016;37(3):1148-1161. doi:10.1002/hbm.23091.
34. Spankovich C, et al. Associations between Diet and Both High and Low Pure Tone Averages and Transient Evoked Otoacoustic Emissions in an Older Adult Population-Based Study. Journal of the American Academy of Audiology, 2011;22(1):49-58. doi: 10.3766/jaaa.22.1.6.
35. Telmesani LM, and Said NM. Electrically evoked compound action potential (ECAP) in cochlear implant children: Changes in auditory nerve response in first year of cochlear implant use. International Journal of Pediatric Otorhinolaryngology, 2016;82(3):28-33. doi:10.1016/j.ijporl.2015.12.027.
36. Toranzo IV, and Dehesa JS. Renyi, Shannon and Tsallis entropies of Rydberg hydrogenic systems. Epl, 2016;113(4), Article ID: 48003. doi:10.1209/0295-5075/113/48003.
37. Valaitis V, et al. Minimizing Hexapod Robot Foot Deviations Using Multilayer Perceptron. International Journal of Advanced Robotic Systems, 2015;12:8, Article ID: 182. doi:10.5772/61675.
38. Wang, S, et al. Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. International Journal of Imaging Systems and Technology, 2015;25(2):153-164. doi:10.1002/ima.22132.
39. Wei L. Fruit classification by wavelet-entropy and feedforward neural network trained by fitnessscaled chaotic ABC and biogeography-based optimization. Entropy, 2015;17(8):5711-5728. doi:10.3390/e17085711.
40. Wei L, and Yang J. Fitness-scaling adaptive genetic algorithm with local search for solving the Multiple Depot Vehicle Routing Problem. SIMULATION, 2016;92(7):601-616. doi:10.1177/0037549715603481.
41. Weimann S, et al. Implementation of quantum and classical discrete fractional Fourier transforms. Nature Communications, 2016;7:8, Article ID: 11027. doi:10.1038/ncomms11027.
42. Wu L. Classification of fruits using computer vision and a multiclass support vector machine. Sensors, 2012;12(9):12489-12505. doi:10.3390/s120912489.
43. Wu L. UCAV path planning by fitness-scaling adaptive chaotic particle swarm optimization. Mathematical Problems in Engineering, 2013, Article ID: 705238. doi:10.1155/2013/705238.
44. Yang X, et al. Pathological Brain Detection by a Novel Image FeatureFractional Fourier Entropy. Entropy, 2015;17(12):8278-8296. doi:10.3390/e17127877.
45. Yeh WC, et al. Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization. International Journal of Electrical Power & Energy Systems, 2014;55:741-748. doi:10.1016/j.ijepes.2013.10.001.
46. Yuan TF. Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning. Frontiers in Computational Neuroscience, 2015;9, Article ID: 66. doi:10.3389/fncom.2015.00066.
47. Zhang YD, et al. Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. International Journal of Imaging Systems and Technology, 2015;25(4):317-327. doi:10.1002/ima.22144.
48. Zhang Y and Wu L. Weights optimization of neural network via improved BCO approach. Progress in Electromagnetics Research, 2008;83:185-198.
49. Zhang YD, et al. Computer-aided diagnosis of abnormal breasts in mammogram images by weightedtype fractional Fourier transform. Advances in Mechanical Engineering, 2016;8(2):11. doi:10.1177/1687814016634243.
50. Zhang YT, et al. Auditory cortical responses evoked by pure tones in healthy and sensorineural hearing loss subjects: functional MRI and magnetoencephalography. Chinese Medical Journal, 2006;119(18):1548-1554. URL www.medscape.com/medline/abstract/16996009.