Diabetic plantar pressure analysis using image fusion

Abstract

Plantar pressure images analysis is the key issue of designing comfortable shoe products through last customizing system, which has attracted the researchers’ curiosity toward image fusion as an application of medical and industrial imaging. In the current work, image fusion has been applied using wavelet transform and compared with Laplace Pyramid. Using image fusion rules of Mean-Max, we presented a plantar pressure image fusion method employing haar wavelet transform. It was compared in different composition layers with the Laplace pyramid transform. The experimental studies deployed the haar, db2, sym4, coif2, and bior5.5 wavelet basis functions for image fusion under decomposition layers of 3, 4, and 5. Evaluation metrics were measured in the case of the different layer number of wavelet decomposition to determine the best decomposition level and to evaluate the fused image quality using with different wavelet functions. The best wavelet basis function and decomposition layers were selected through the analysis and the evaluation measurements. This study established that haar wavelet transform with five decomposition levels on plantar pressure image achieved superior performance of 89.2817% mean, 89.4913% standard deviation, 5.4196 average gradient, 14.3364 spatial frequency, 5.9323 information entropy and 0.2206 cross entropy.

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References

  1. 1.

    Angari HMA, Khandoker AH, Lee S, Almahmeed W, Safar HSA, Jelinek HF, Khalaf K (2017) Novel dynamic peak and distribution plantar pressure measures on diabetic patients during walking. Gait Posture 51:261–267

    Article  Google Scholar 

  2. 2.

    Banerjee A, Dutta A (2013) Performance comparison of cosine, haar, walsh-hadamard, fourier and wavelet transform for shape based image retrieval using fuzzy similarity measure. Proc Tech 10:623–627

    Article  Google Scholar 

  3. 3.

    Bennetts CJ, Owings TM, Erdemir A, Botek G, Cavanagh PR (2013) Clustering and classification of regional peak plantar pressures of diabetic feet. J Biome 46(1):19–25

    Article  Google Scholar 

  4. 4.

    Bhandari KA, Ramchandra RM (2016) An innovative remote sensing image retrieval techniques based on haar wavelet-LTRP and ANFIS. Proc Comp Sci 79:391–401

    Article  Google Scholar 

  5. 5.

    Bhosale B , Moraru L, Ahmed BS, Riser D, Biswas A (2014) Multi-resolution analysis of wavelet like soliton solution of KDY equation. Proceedings of the Romanian academy, Series A 15(1):18–26

    MathSciNet  Google Scholar 

  6. 6.

    Chang Z, Ban X, Wang Y (2016) Fatigue driving detection based on Haar feature and extreme learning machine. J China Uni Posts Telecomm 23(4):91–100

    Article  Google Scholar 

  7. 7.

    Chatzistergos PE, Naemi R, Chockalingam N (2014) An MRI compatible loading device for the reconstruction of clinically relevant plantar pressure distributions and loading scenarios of the forefoot. Med Eng Phy 36(9):1205–1211

    Article  Google Scholar 

  8. 8.

    Chen W-M, Lee S-J, Lee PVS (2015) Plantar pressure relief under the metatarsal heads - Therapeutic insole design using three-dimensional finite element model of the foot. J Biomech 48(4):659–665

    Article  Google Scholar 

  9. 9.

    Deschamps K, Staes F, Desmet D, Roosen P, Matricali GA, Keijsers N, Nobels F, Tits J, Bruyninckx H (2015) A color-code based method for the interpretation of plantar pressure measurements in clinical gait analysis. Gait Posture 41(3):852–856

    Article  Google Scholar 

  10. 10.

    Domnguez HJO, Mynez LO, Villegas OOV, Mederos B, Meja JM, Snchez VGC (2015) Denoising of high resolution small animal 3D PET data using the non-subsampled Haar wavelet transform Nuclear Instruments and Methods in Physics Research Section A: Accelerators, spectrometers. Dete Ass Equipt 784:581–584

    Article  Google Scholar 

  11. 11.

    Frye TP, George AK, Kilchevsky A, Maruf M, Siddiqui MM, Kongnyuy M, Muthigi A, Han H, Parnes HL, Merino M, Choyke PL, Turkbey B, Wood B, Pinto PA (2017) Magnetic resonance Imaging-Transrectal ultrasound guided fusion biopsy to detect progression in patients with existing lesions on active surveillance for low and intermediate risk prostate cancer. J Urol 197(3):640–646

    Article  Google Scholar 

  12. 12.

    He C, Shao J, Xu X, Ouyang D, Gao L (2017) Exploiting score distribution for heterogenous feature fusion in image classification, Neurocomputing. https://doi.org/10.1016/j.neucom.2016.09.129, Accessed April 2017

  13. 13.

    Kamal S, Chowdhury L, Khan MI, Ashour AS, Tavares JMRS, Dey N (2017) Hidden markov model and chapman kolmogrov for protein structures prediction from images. Comput Biol Chem 68:231–244

    Article  Google Scholar 

  14. 14.

    Kamal S, Dey N, Nimmy SF, Ripon SH, Ali NY, Ashour AS, Karaa WBA, Shi F (2018) Evolutionary framework for coding area selection from cancer data. Neural Comput Applic 29(4):1015–1037

    Article  Google Scholar 

  15. 15.

    Kamal S, Parvin S, Ashour AS, Shi F, Dey N (2017) De-bruijn graph with MapReduce framework towards metagenomic data classification. Int J Inf Technol 9:59–75

    Google Scholar 

  16. 16.

    Kamal S, Ripon SH, Dey N, Ashour AS, Santhi V (2016) A mapreduce approach to diminish imbalance parameters for big deoxyribonucleic acid dataset. Comput Methods Prog Biomed 131:191–206

    Article  Google Scholar 

  17. 17.

    Keijsers NLW, Stolwijk NM, Louwerens JWK, Duysens J (2013) Classification of forefoot pain based on plantar pressure measurements. Clin Biome 28(3):350–356

    Article  Google Scholar 

  18. 18.

    Koc A, Bartan B, Gundogdu E, cukur T, Ozaktas HM (2017) Sparse representation of two- and three-dimensional images with fractional Fourier, Hartley, linear canonical, and Haar wavelet transforms. Exp Syst Appl 77:247–255

    Article  Google Scholar 

  19. 19.

    Li Z, Balas VE, McCauley P, Shi F (2015) Multi-source information fusion model in rule-based fuzzy inference system incorporating gaussian density function. J Intel Fuzzy Syst 29:2335–2344

    Article  Google Scholar 

  20. 20.

    Li Z, Dey N, Ashour AS, Cao L, Wang Y, Wang D, McCauley P, Balas VE, Shi K, Shi F (2017) Convolutional neural network based clustering and manifold learning method for diabetic plantar pressure imaging dataset. J Med Imag and Health Inform 7:1–14

    Article  Google Scholar 

  21. 21.

    Liu Y, Chen X, Peng H, Wang Z (2017) Multi-focus image fusion with a deep convolutional neural network. Inform Fusion 36:191–207

    Article  Google Scholar 

  22. 22.

    Mallat SG, Zhang Z (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal 11:674–691

    Article  Google Scholar 

  23. 23.

    Mohamed B, Issam A, Mohamed A, Abdellatif B (2015) ECG image classification in real time based on the haar-like features and artificial neural networks. Proc Comp Sci 73:32–39

    Article  Google Scholar 

  24. 24.

    Moraru L, Bibicu D, Biswas A (2013) Standalone functional CAD system for multi-object case analysis in hepatic disorders. Comput Biol Med 43:967–974

    Article  Google Scholar 

  25. 25.

    Oliva D, Hinojosa S, Cuevas E, Pajares G, Avalos O, Glvez J. (2017) Cross entropy based thresholding for magnetic resonance brain images using Crow Search Algorithm. Exp Syst Appl 79:164–180

    Article  Google Scholar 

  26. 26.

    Piras L, Giacinto G (2017) Information fusion in content based image retrieval: a comprehensive overview. Inform Fusion 37:50–60

    Article  Google Scholar 

  27. 27.

    Qian J, Yang J, Tai Y, Zheng H (2016) Exploring deep gradient information for biometric image feature representation. Neurocomputing 213:162–171

    Article  Google Scholar 

  28. 28.

    Ray SS, Patra A (2013) Haar wavelet operational methods for the numerical solutions of fractional order nonlinear oscillatory Van der Pol system. Appl Math Comp 220:659–667

    MathSciNet  Article  Google Scholar 

  29. 29.

    Tedmori S, Najdawi NA (2014) Image cryptographic algorithm based on the Haar wavelet transform. Inform Sci 269:21–34

    MathSciNet  Article  Google Scholar 

  30. 30.

    Valerio M, Shah TT, Shah P, Mccartan N, Emberton M, Arya M, Ahmed HU (2017) Magnetic resonance imaging-transrectal ultrasound fusion focal cryotherapy of the prostate: a prospective development study. Urol Oncol 35(4):150.e1–150.e7

    Article  Google Scholar 

  31. 31.

    Wang Z, Gong C (2017) A multi-faceted adaptive image fusion algorithm using a multi-wavelet-based matching measure in the PCNN domain. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2017.02.03519 Accessed March 2017

  32. 32.

    Wang D, Li Z, Cao L, Balas VE, Dey N, Ashour AS, McCauley P, Shi F (2017) Multi-scale plantar pressure imaging data fusion incorporating improved Gaussian mixture operator and fuzzy weighted evaluation system. IEEE Sensors J 17 (5):1407–1420

    Article  Google Scholar 

  33. 33.

    Wang D, Li Z, Dey N, Ashour AS, Sherratt RS, Shi F (2017) Case-Based Reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables. IEEE Access 5:4900–4912

    Article  Google Scholar 

  34. 34.

    Zhang Y, Bai X, Wang T (2017) Boundary finding based multi-focus image fusion through multi-scale morphological focus-measure. Inform Fusion 35:81–101

    Article  Google Scholar 

  35. 35.

    Zhou W, Yu L, Qiu W, Zhou Y, Wu M (2017) Local gradient patterns (LGP): An effective local-statistical-feature extraction scheme for no-reference image quality assessment. Inform Sci 397–398:1–14

    Article  Google Scholar 

  36. 36.

    Zong J, Qiu T (2017) Medical image fusion based on sparse representation of classified image patches. Biomedi Signal Proces 34:195–205

    Article  Google Scholar 

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Correspondence to Fuqian Shi.

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This work is partial supported by Zhejiang Provincial Natural Science Foundation under Grant (LY17F030014), the National Natural Science Foundation of China (Grant nos: 81271663 and 31471146), Zhejiang Wenzhou Medical University Scientific Development Foundation of China (Grant no: QTJ06012).

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Cao, L., Dey, N., Ashour, A.S. et al. Diabetic plantar pressure analysis using image fusion. Multimed Tools Appl 79, 11213–11236 (2020). https://doi.org/10.1007/s11042-018-6269-x

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Keywords

  • Biomedical signal processing
  • Image fusion
  • Plantar pressure sensors
  • Wavelet transforms
  • Gaussian laplace pyramid