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Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies

  • Computer aided Medical Diagnosis
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Abstract

Plantar pressure imaging technologies play more important role in diabetics’ shoe-last design. The plantar pressure imaging data-set was acquired through pressure sensors system and was preprocessed using typical image process technologies including Gauss filtering, gamma correction, and wavelet transform enhancement in this work. To decrease the computational complexity, edge detection operator with Sobel, Roberts, Prewitt, Log and Canny were applied. Finally, threshold-based method, gray, watershed, feature clustering and fuzzy cluster, region growing-based plantar pressure image segmentation were employed, respectively. Results illustrated that threshold performs the better effectiveness by using analytic hierarchy process evaluation method through a special indices definition. The proposed methods in the research will be potential application and guidance for comfort shoe design for diabetics.

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References

  1. Sodhro AH, Kumar A (2018) An energy-efficient algorithm for wearable electrocardiogram signal processing in ubiquitous 3 healthcare applications. MDPI Sens 8(3):923–943

    Article  Google Scholar 

  2. Sodhro AH (2018) 5G-based transmission power control mechanism in fog computing for IoT devices. MDPI Sustain 10(4):1–17

    Google Scholar 

  3. 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 2017:4900–4912

    Article  Google Scholar 

  4. Li Z, Valentina B, Pamela MB, Shi F (2015) Multi-source Information fusion model in rule-based fuzzy inference system incorporating gaussian density function. J Intell Fuzzy Syst 29:2335–2344

    Article  MATH  Google Scholar 

  5. Wang C, Li Z, Dey N, Ashour AS, Fong SJ, Sherratt RS, Wu L, Shi F (2018) Histogram of oriented gradient based plantar pressure image feature extraction and classification employing fuzzy support vector machine. J Med Imaging Health Inform 8(4):842–854

    Article  Google Scholar 

  6. Roscoe D, Roberts AJ, Hulse D, Shaheen A, Hughes MP, Bennett A (2018) Barefoot plantar pressure measurement in Chronic Exertional Compartment Syndrome. Gait Posture 63:10–16

    Article  Google Scholar 

  7. Buldt AK, Allan JJ, Landorf KB, Menz HB (2018) The relationship between foot posture and plantar pressure during walking in adults: a systematic review. Gait Posture 62:56–67

    Article  Google Scholar 

  8. Booth BG, Keijsers NLW, Sijbers J, Huysmans T (2018) STAPP: Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping. Gait Posture 63:268–275

    Article  Google Scholar 

  9. Stewart S, Carroll M, Brenton-Rule A, Keys M, Bell L, Dalbeth N, Rome K (2018) Region-specific foot pain and plantar pressure in people with rheumatoid arthritis: a cross-sectional study. Clin Biomech 55:14–17

    Article  Google Scholar 

  10. Claverie L, Ille A, Moretto P (2016) Discrete sensors distribution for accurate plantar pressure analyses. Med Eng Phys 38(12):1489–1494

    Article  Google Scholar 

  11. Bousie JA, Blanch P, McPoil TG, Vicenzino B (2018) Hardness and posting of foot orthoses modify plantar contact area, plantar pressure, and perceived comfort when cycling. J Sci Med Sport 21(7):691–696

    Article  Google Scholar 

  12. Khodaei B, Saeedi H, Jalali M, Farzadi M, Norouzi E (2017) Comparison of plantar pressure distribution in CAD–CAM and prefabricated foot orthoses in patients with flexible flatfeet. The Foot 33:76–80

    Article  Google Scholar 

  13. Hafer JF, Lenhoff MW, Song J, Jordan JM, Hannan MT, Hillstrom HJ (2013) Reliability of plantar pressure platforms. Gait Posture 38(3):544–548

    Article  Google Scholar 

  14. van Netten JJ, van Baal JG, Bril A, Wissink M, Bus SA (2018) An exploratory study on differences in cumulative plantar tissue stress between healing and non-healing plantar neuropathic diabetic foot ulcers. Clin Biomech 53:86–92

    Article  Google Scholar 

  15. Yick KL, Tse LT, Lo WT, Ng SP, Yip J (2016) Effects of indoor slippers on plantar pressure and lower limb EMG activity in older women. Appl Ergon 56:153–159

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Kim HK, Mirjalili SA, Fernandez J (2018) Gait kinetics, kinematics, spatiotemporal and foot plantar pressure alteration in response to long-distance running: systematic review. Hum Mov Sci 57:342–356

    Article  Google Scholar 

  18. Etehadtavakol M, Ng EYK, Kaabouch N, Lin C-H, Qiu Z-H, Yeh C-C (2018) Image processing for rear foot image evaluating leg and foot angles. Measurement 126:168–183

    Article  Google Scholar 

  19. Etehadtavakol M, Ng EYK, Kaabouch N (2017) Automatic segmentation of thermal images of diabetic-at-risk feet using the snakes algorithm. Infrared Phys Technol 86:66–76

    Article  Google Scholar 

  20. Adam M, Ng EYK, Oh SL, Heng ML, Hagiwara Y, Tan JH, Tong JWK, Acharya UR (2018) Automated characterization of diabetic foot using nonlinear features extracted from thermograms. Infrared Phys Technol 89:325–337

    Article  Google Scholar 

  21. Wang B, Chen LL, Cheng J (2018) New result on maximum entropy threshold image segmentation based on P system. Optik 163:81–85

    Article  Google Scholar 

  22. Garcia-Lamont F, Cervantes J, López A, Rodriguez L (2018) Segmentation of images by color features: a survey. Neurocomputing 292:1–27

    Article  Google Scholar 

  23. Min H, Lu J, Jia W, Zhao Y, Luo Y (2018) An effective local regional model based on salient fitting for image segmentation. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.05.070

    Article  Google Scholar 

  24. Akbulut Y, Guo Y, Şengür A, Aslan M (2018) An effective color texture image segmentation algorithm based on hermite transform. Appl Soft Comput 67:494–504

    Article  Google Scholar 

  25. Matić T, Aleksi I, Hocenski Ž, Kraus D, Nausheen N, Seal A, Khanna P, Halder S (2018) A FPGA based implementation of Sobel edge detection. Microprocess Microsyst 56:84–91

    Article  Google Scholar 

  26. Matić T, Aleksi I, Hocenski Ž, Kraus D (2018) Real-time biscuit tile image segmentation method based on edge detection. ISA Trans 76:246–254

    Article  Google Scholar 

  27. Akinlar C, Topal C (2017) ColorED: color edge and segment detection by Edge Drawing (ED). J Vis Commun Image Represent 44:82–94

    Article  Google Scholar 

  28. Li J, Tang W, Wang J, Zhang X (2018) Multilevel thresholding selection based on variational mode decomposition for image segmentation. Sig Process 147:80–91

    Article  Google Scholar 

  29. Healy S, McMahon J, Owens P, Dockery P, FitzGerald U (2018) Threshold-based segmentation of fluorescent and chromogenic images of microglia, astrocytes and oligodendrocytes in FIJI. J Neurosci Methods 295:87–103

    Article  Google Scholar 

  30. Eltanboly A, Ghazal M, Hajjdiab H, Shalaby A, Switala A, Mahmoud A, Sahoo P, El-Azab M, El-Baz A (2019) Level sets-based image segmentation approach using statistical shape priors. Appl Math Comput 340:164–179

    MathSciNet  MATH  Google Scholar 

  31. Kim JJ, Nam J, Jang IG (2018) Fully automated segmentation of a hip joint using the patient-specific optimal thresholding and watershed algorithm. Comput Methods Programs Biomed 154:161–171

    Article  Google Scholar 

  32. 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 Imaging Health Inform 7(3):639–652

    Article  Google Scholar 

  33. Zhang Y, Guo H, Chen F, Yang H (2017) Weighted kernel mapping model with spring simulation based watershed transformation for level set image segmentation. Neurocomputing 249:1–18

    Article  Google Scholar 

  34. Angelin AF, Da Silva FM, Barbosa LAG, Lintz RCC, De Carvalho MAG, Franco RAS (2017) Voids identification in rubberized mortar digital images using K-Means and Watershed algorithms. J Clean Prod 164:455–464

    Article  Google Scholar 

  35. Goswami S, Das AK, Chakrabarti A, Chakraborty B (2017) A feature cluster taxonomy based feature selection technique. Expert Syst Appl 79:76–89

    Article  Google Scholar 

  36. Chormunge S, Jena S (2018) Correlation based feature selection with clustering for high dimensional data. J Electric Syst Inf Technol 5:5–9. https://doi.org/10.1016/j.jesit.2017.06.004

    Article  Google Scholar 

  37. Wang F, Liu Y, Chen W, Chen X, Zeng K (2018) Spot image ablated by femtosecond laser segmentation and feature clustering after dimension reduction reconstruction. Optik 164:488–497

    Article  Google Scholar 

  38. Agapova M, Bresnahan BW, Linnau KF, Garrison LP, Higashi M, Kessler L, Devine B (2017) Using the analytic hierarchy process for prioritizing imaging tests in diagnosis of suspected appendicitis. Acad Radiol 24(5):530–537

    Article  Google Scholar 

  39. Di Angelo L, Di Stefano P, Fratocchi L, Marzola A (2018) An AHP-based method for choosing the best 3D scanner for cultural heritage applications. J Cult Heritage. https://doi.org/10.1016/j.culher.2018.03.026

    Article  Google Scholar 

  40. Saaty TL (1980) The analytic hierarchy process. McGraw-Hill Company, New York

    MATH  Google Scholar 

Download references

Acknowledgements

VHCA thanks CNPq via Grant 304315/2017-6.

Funding

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

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Authors and Affiliations

  1. Department of Orthopedics, Yiwu Central Hospital, Wenzhou Medical University, Wenzhou, People’s Republic of China

    Hongwei Chen

  2. Institute of Digitized Medicine, Wenzhou Medical University, Wenzhou, People’s Republic of China

    Luying Cao, Lijun Wu & Fuqian Shi

  3. Tianjin Key Laboratory of Process, Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, People’s Republic of China

    Zairan Li

  4. Department of ECE, Karunya University, Coimbatore, 641114, India

    D. Jude Hemanth

  5. Programa de Pós-Graduação em Informática Aplicada, Universidade de Fortaleza, Fortaleza, Ceará, Brazil

    Victor Hugo C. de Albuquerque

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  1. Hongwei Chen
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  2. Luying Cao
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  7. Fuqian Shi
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Correspondence to D. Jude Hemanth or Fuqian Shi.

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Chen, H., Cao, L., Li, Z. et al. Evaluation on diabetic plantar pressure data-set employing auto-segmentation technologies. Neural Comput & Applic 32, 11041–11054 (2020). https://doi.org/10.1007/s00521-018-3838-x

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  • DOI: https://doi.org/10.1007/s00521-018-3838-x

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