PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Pathological Brain Detection via Wavelet Packet Tsallis Entropy and Real-Coded Biogeography-based Optimization

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
(Aim) In order to detect pathological brains in a more efficient way, (Method) we proposed a novel system of pathological brain detection (PBD) that combined wavelet packet Tsallis entropy (WPTE), feedforward neural network (FNN), and real-coded biogeography-based optimization (RCBBO). (Results) The experiments showed the proposed WPTE + FNN + RCBBO approach yielded an average accuracy of 99.49% over a 255-image dataset. (Conclusions) The WPTE + FNN + RCBBO performed better than 10 state-of-the-art approaches.
Wydawca
Rocznik
Strony
275--291
Opis fizyczny
Bibliogr. 52 poz., fot., rys., tab., wykr.
Twórcy
autor
  • School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
autor
  • School of Control Science and Engineering, Shandong University, Jinan, 205100, China
autor
  • Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
autor
  • School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443, USA
autor
  • Department of Psychiatry, College of Physicians & Surgeons, Columbia University, New York, NY 10032, USA
autor
  • School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
autor
  • School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu 210046, China
Bibliografia
  • [1] Wang S, Dong S, Ji G, (et al.). Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progress In Electromagnetics Research. 2014; 144: 171-184. doi: 10.2528/PIER13121310. Available from: http://www.jpier.org/pier/pier.php?paper=13121310.
  • [2] Liu SL, Oyama T, Miyoshi Y, (et al.). Establishment of a two-dimensional chiral HPLC system for the simultaneous detection of lactate and 3-hydroxybutyrate enantiomers in human clinical samples. Journal of Pharmaceutical and Biomedical Analysis. 2015; 116: 80-85. doi: 10.1016/j.jpba.2015.05.036.
  • [3] Chen Y, Yang J, Cao Q, (et al.). Curve-like structure extraction using minimal path propagation with back-tracing. IEEE Transactions on Image Processing. 2016; 25 (2): 988-1003.
  • [4] Dong Z, Phillips P, Wang S, (et al.). Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Information Sciences. 2015; 322 (0): 115-132. doi: 10.1016/j.ins.2015.06.017.
  • [5] Minamimoto R, Loening A, Jamali M, (et al.). Prospective Comparison of Tc-99m-MDP Scintigraphy, Combined F-18-NaF and F-18-FDG PET/CT, and Whole-Body MRI in Patients with Breast and Prostate cancer. Journal of Nuclear Medicine. 2015; 56 (12): 1862-1868. doi: 10.2967/jnumed.115.162610.
  • [6] Martins EBL, Chojniak R, Kowalski LP, (et al.). Diffusion-Weighted MRI in the Assessment of Early Treatment Response in Patients with Squamous-Cell Carcinoma of the Head and Neck: Comparison with Morphological and PET/CT Findings. Plos One. 2015; 10 (11): 12. doi: 10.1371/journal.pone.0140009.
  • [7] Yu D, Shui H, Gen L, (et al.). Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging. IEEJ Transactions on Electrical and Electronic Engineering. 2015; 10 (1): 116-117. doi: 10.1002/tee.22059.
  • [8] Ma J, Sun DW, Qu JH, (et al.). Applications of Computer Vision for Assessing Quality of Agri-food Products: A Review of Recent Research Advances. Critical Reviews in Food Science and Nutrition. 2016; 56 (1): 113-127. doi: 10.1080/10408398.2013.873885.
  • [9] DeCost BL, Holm EA. A computer vision approach for automated analysis and classification of microstructural image data. Computational Materials Science. 2015; 110: 126-133. doi: 10.1016/j.commatsci.2015.08.011.
  • [10] Wang S, Yang X, Zhang Y, (et al.). Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy. 2015; 17 (10): 6663-6682. doi: 10.3390/e17106663.
  • [11] de Oliveira EM, Leme DS, Barbosa BHG, (et al.). A computer vision system for coffee beans classification based on computational intelligence techniques. Journal of Food Engineering. 2016; 171: p. 22-27. doi: 10.1016/j.jfoodeng.2015.10.009.
  • [12] El-Dahshan ESA, Hosny T, and Salem ABM. Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing. 2010; 20 (2): 433-441. doi: 10.1016/j.dsp.2009.07.002.
  • [13] Dong Z, Wu L, Wang S, (et al.). A hybrid method for MRI brain image classification. Expert System with Applications. 2011; 38 (8): 10049-10053. doi: 10.1016/j.eswa.2011.02.012.
  • [14] Das S, Chowdhury M, and Kundu MK. Brain MR image classification using multiscale geometric analysis of Ripplet. Progress in Electromagnetics Research-Pier. 2013; 137: 1-17. doi: 10.2528/PIER13010105. Available from: http://www.jpier.org/pier/pier.php?paper=13010105.
  • [15] Zhang Y and Wu L. An MR brain images classifier via principal component analysis and kernel support vector machine. Progress In Electromagnetics Research. 2012; 130: 369-388. doi: 10.2528/PIER12061410. Available from: http://www.jpier.org/pier/pier.php?paper=12061410.
  • [16] Saritha M, Joseph K Paul, and Mathew AT. Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognition Letters. 2013; 34 (16): 2151-2156. doi: 10.1016/j.patrec.2013.08.017.
  • [17] El-Dahshan ESA, Mohsen HM, Revett K, (et al.). Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications. 2014; 41 (11): 5526-5545. doi: 10.1016/j.eswa.2014.01.021.
  • [18] Wang S, Dong Z, Du 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.
  • [19] Sun P, Wang S, Phillips P, (et al.). Pathological brain detection based on wavelet entropy and Hu moment invariants. Bio-Medical Materials and Engineering. 2015; 26 (s1): 1283-1290. doi: 10.3233/BME-151426.
  • [20] Wibmer A, Hricak H, Gondo T, (et al.). Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. European Radiology. 2015; 25 (10): 2840-2850. doi: 10.1007/s00330-015-3701-8.
  • [21] Zhang Y, Dong Z, Wang S, (et al.). 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. 2015; 17 (4): 1795-1813. doi: 10.3390/e17041795.
  • [22] Dong Z, Liu A, Wang S, (et al.). Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. Journal of Medical Imaging and Health Informatics. 2015; 5 (7): 1395-1403. doi: 10.1166/jmihi.2015.1542.
  • [23] Karim SAA, Ismail MT, Hasan MK, (et al.). Electroencephalography Data Analysis by Using Discrete Wavelet Packet Transform, in International Conference on Mathematics, Engineering and Industrial Applications 2014, M. F. Ramli, et al., Editors. 2015; Amer Inst Physics: Melville, doi: 10.1063/1.4915737.
  • [24] Suma MN, Narasimhan SV, Kanmani B. Interspersed discrete harmonic wavelet packet transform based OFDM - IHWT OFDM. International Journal of Wavelets Multiresolution and Information Processing. 2014; 12 (3): 11. doi: 10.1142/S0219691314500349.
  • [25] Gokmen G. The defect detection in glass materials by using discrete wavelet packet transform and artificial neural network. Journal of Vibroengineering. 2014; 16 (3): 1434-1443.
  • [26] Belhumeur PN, Hespanha JP, Kriegman D, Eigenfaces vs. Fisherfaces: recognition using class specific linear prejection. Pattern Analysis and Machine Intelligence, IEEE Transactions on. 1997; 19 (7): 711-720. doi: 10.1109/34.598228.
  • [27] Tsallis C. Nonadditive entropy: The concept and its use. European Physical Journal A. 2009; 40 (3): 257-266. doi: 10.1140/epja/i2009-10799-0.
  • [28] Zhang Y. and Wu L. Optimal multi-level Thresholding based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach. Entropy. 2011; 13 (4): 841-859.
  • [29] Nayak AS, Sudha A, Rajagopal AK, (et al.). Bipartite separability of symmetric N-qubit noisy states using conditional quantum relative Tsallis entropy. Physica a-Statistical Mechanics and Its Applications. 2016; 443: 286-295. doi: 10.1016/j.physa.2015.09.086.
  • [30] Bhandari AK, Kumar A, and Singh GK. Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Systems with Applications. 2015; 42 (22): 8707- 8730. doi: 10.1016/j.eswa.2015.07.025.
  • [31] Singh VP and Oh J. A Tsallis entropy-based redundancy measure for water distribution networks. Physica a-Statistical Mechanics and Its Applications. 2015; 421: 360-376. doi: 10.1016/j.physa.2014.11.044.
  • [32] Amaral-Silva H, Wichert-Ana L, Otavio Murta L, (et al.). The Superiority of Tsallis Entropy over Traditional Cost Functions for Brain MRI and SPECT Registration. Entropy. 2014; 16 (3): 1632-1651. doi: 10.3390/e16031632.
  • [33] Venkatesan AS and Parthiban L. A Novel Nature Inspired Fuzzy Tsallis Entropy Segmentation of Magnetic Resonance Images. Neuroquantology. 2014; 12 (2): 221-229. doi: 10.14704/nq.2014.12.2.733.
  • [34] Khader M and Ben Hamza A. Nonrigid Image Registration Using an Entropic Similarity. IEEE Transactions on Information Technology in Biomedicine. 2011; 15 (5): 681-690. doi: 10.1109/TITB.2011.2159806.
  • [35] Chandwani V, Agrawal V, and Nagar R. Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks. Expert Systems with Applications. 2015; 42 (2): 885-893. doi: 10.1016/j.eswa.2014.08.048.
  • [36] Wu L and Wang S. Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm. Progress in Electromagnetics Research. 2011; 116: 65-79. doi: 10.2528/PIER11031709. Available from: http://www.jpier.org/pier/pier.php?paper=11031709.
  • [37] Manoochehri M and Kolahan F. Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process. International Journal of Advanced Manufacturing Technology. 2014; 73 (l-4): 241-249. doi: 10.1007/s00170-014-5788-5.
  • [38] Momeni E, Armaghani DJ, Hajihassani M, (et al.). Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement. 2015; 60: 50-63. doi: 10.1016/j.measurement.2014.09.075.
  • [39] Wang S, Zhang Y, Ji G, (et al.). Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy. 2015; 17 (8): 5711-5728. doi: 10.3390/e17085711.
  • [40] Yosef M, Sayed MM, and Youssef HKM. Allocation and sizing of distribution transformers and feeders for optimal planning of MV/LV distribution networks using optimal integrated biogeography based optimization method. Electric Power Systems Research. 2015; 128: 100-112. doi: 10.1016/j.epsr.2015.06.022.
  • [41] Garg H. An efficient biogeography based optimization algorithm for solving reliability optimization problems. Swarm and Evolutionary Computation. 2015; 24: 1-10. doi: 10.1016/j.swevo.2015.05.001.
  • [42] Kim SS, Byeon JH, Lee S, (et al.). A grouping biogeography-based optimization for location area planning. Neural Computing & Applications. 2015; 26 (8): 2001-2012. doi: 10.1007/s00521-015-1856-5.
  • [43] Kumar AR and Premalatha L. Optimal power flow for a deregulated power system using adaptive real coded biogeography-based optimization. International Journal of Electrical Power & Energy Systems. 2015; 73: 393-399. doi: 10.1016/j.ijepes.2015.05.011.
  • [44] Goudos SK, Piets D, Liu N, (et al.). A multi-objective approach to indoor wireless heterogeneous networks planning based on biogeography-based optimization. Computer Networks. 2015; 91: 564-576. doi: 10.1016/j.comnet.2015.08.037. Available from: http://hdl.handle.net/1854/LU-7091375.
  • [45] Simon D. A probabilistic analysis of a simplified biogeography-based optimization algorithm. Evolutionary Computation. 2011; 19 (2): 167-188. doi: 10.1162/EVCO_a_00018.
  • [46] Ma HP, Su SF, Simon D, (et al.). Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling. Engineering Applications of Artificial Intelligence. 2015; 44: 79-90. Available from: http://dx.doi.org/10.1016/j.engappai.2015.05.009.
  • [47] Gong WY, Cai ZH, Ling CX, (et al.). A real-coded biogeography-based optimization with mutation. Applied Mathematics and Computation. 2010; 216 (9): 2749-2758. doi: 10.1016/j.amc.2010.03.123.
  • [48] Kumar AR and Premalatha L. Real Coded Biogeography-Based Optimization for Environmental Constrained Dynamic Optimal Power Flow. Journal of Electrical Engineering & Technology. 2015; 10 (1): 56-63. Available from: http://dx.doi.org/10.5370/JEET.2015.10.1.056.
  • [49] Purushotham S and Tripathy BK. Evaluation of Classifier Models Using Stratified Tenfold Cross Validation Techniques, in Global Trends in Information Systems and Software Applications. Pt 2, PV. Krishna, M. R. Babu, and E. Ariwa, Editors. 2012, Springer-Verlag Berlin: Berlin, p. 680-690. doi: 10.1007/978-3-642-29216-3_74.
  • [50] Chatterjee A, Siarry P, Nakib A, (et al.). An improved biogeography based optimization approach for segmentation of human head CT-scan images employing fuzzy entropy. Engineering Applications of Artificial Intelligence. 2012; 25 (8): 1698-1709. doi: 10.1016/j.engappai.2012.02.007.
  • [51] Sturzbecher MJ, Tedeschi W, Cabella BCT, (et al.). Non-extensive entropy and the extraction of BOLD spatial information in event-related functional MRI. Physics in Medicine and Biology. 2009; 54 (1): 161-174. doi: 10.1088/0031-9155/54/1/011.
  • [52] Cabella BCT, Sturzbecher MJ, de Araujo DB, (et al.). Generalized relative entropy in functional magnetic resonance imaging. Physica a-Statistical Mechanics and Its Applications. 2009; 388 (1): 41-50. doi: 10.1016/j.physa.2008.09.029.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a19bfa9a-23a1-4091-b7cc-1b67e83b6d17
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.