Robust Edge Detection Method for Segmentation of Diabetic Foot Ulcer Images
Segmentation is an open-ended research problem in various computer vision and image processing tasks. This pre-processing operation requires a robust edge detector to generate appealing results. However, the available approaches for edge detection underperform when applied to images corrupted by noise or impacted by poor imaging conditions. The problem becomes significant for images containing diabetic foot ulcers, which originate from people with varied skin color. Comparative performance evaluation of the edge detectors facilitates the process of deciding an appropriate method for image segmentation of diabetic foot ulcers. Our research discovered that the classical edge detectors cannot clearly locate ulcers in images with black-skin feet. In addition, these methods collapse for degraded input images. Therefore, the current research proposes a robust edge detector that can address some limitations of the previous attempts. The proposed method incorporates a hybrid diffusion-steered functional derived from the total variation and the Perona-Malik diffusivities, which have been reported to can effectively capture semantic features in images. The empirical results show that our method generates clearer and stronger edge maps with higher perceptual and objective qualities. More importantly, the proposed method offers lower computational times—an advantage that gives more insights into the possible application of the method in time-sensitive tasks.
“EXECUTIVE SUMMARY GLOBAL REPORT ON DIABETES.”
M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg, and M. H. Yap, “DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification,” 2017. DOI: https://doi.org/10.1109/SMC.2017.8122675
M. A. Ahmed, G. L. Muntingh, and P. Rheeder, “Review Article Perspectives on Peripheral Neuropathy as a Consequence of Metformin-Induced Vitamin B12 Deficiency in T2DM,” 2017. DOI: https://doi.org/10.1155/2017/2452853
C. V. Kumar and V. Malathy, “Image processing based wound assessment system for patients with diabetes using six classification algorithms,” in 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016, pp. 744–747. DOI: https://doi.org/10.1109/ICEEOT.2016.7754782
P. Shah, S. Mahajan, S. Nageswaran, S. K. Paul, and M. Ebenzer, “Non-contact ulcer area calculation system for neuropathic foot ulcer,” Foot Ankle Surg., vol. 25, no. 1, pp. 47–50, Feb. 2019. DOI: https://doi.org/10.1016/j.fas.2017.07.1125
M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg, and H. Yap, “DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification,” 20AD.
H. Thabit and R. Kurmasha, “Enhancement of Edge-based Image Quality Measures Using Entropy for Histogram Equalization-based Contrast Enhancement Techniques,” vol. 7, no. 6, pp. 2277–2281, 2017.
A. Sharma, J. Punjab, I. Pankaj Sharma, I. Rashmi, and I. Hardeep Kumar, “"International Journal for Science and Emerging Technologies with Latest Trends" 4(1): 1-6 (2012) Edge Detection of Medical Images Using Morpholgical Algorithms.”
L. Yazdanpanah et al., “Incidence and Risk Factors of Diabetic Foot Ulcer: A Population-Based Diabetic Foot Cohort (ADFC Study)—Two-Year Follow-Up Study,” Int. J. Endocrinol., vol. 2018, pp. 1–9, Mar. 2018.
M. Goyal, N. D. Reeves, S. Rajbhandari, J. Spragg, and M. H. Yap, “Fully Convolutional Networks for Diabetic Foot Ulcer Segmentation.”
L. Wang et al., “An Automatic Assessment System of Diabetic Foot Ulcers Based on Wound Area Determination, Color Segmentation, and Healing Score Evaluation.,” J. Diabetes Sci. Technol., vol. 10, no. 2, pp. 421–8, Aug. 2015. DOI: https://doi.org/10.1177/1932296815599004
M. Lepäntalo et al., “Chapter V: Diabetic Foot,” Eur. J. Vasc. Endovasc. Surg., vol. 42, pp. S60–S74, Dec. 2011. DOI: https://doi.org/10.1016/S1078-5884(11)60012-9
L. Fraiwan, M. AlKhodari, J. Ninan, B. Mustafa, A. Saleh, and M. Ghazal, “Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study,” Biomed. Eng. Online, vol. 16, no. 1, p. 117, Dec. 2017. DOI: https://doi.org/10.1186/s12938-017-0408-x
C. Liu et al., “Infrared Dermal Thermography on Diabetic Feet Soles to Predict Ulcerations: a Case Study EU FP7 3D Face View project Biomechanical determinants of pain and fatigue in adolescents with hypermobility syndrome and ehlers danlos hypermobility type View project Infrared Dermal Thermography on Diabetic Feet Soles to Predict Ulcerations: a Case Study,” 2013. DOI: https://doi.org/10.1117/12.2001807
R. Maini and & Dr, “Study and Comparison of Various Image Edge Detection Techniques.”
M. Shahnoor and I. Khan, “Implementation of Edge & Shape Detection Techniques and their Performance Evaluation,” 2012.
L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Phys. D Nonlinear Phenom., vol. 60, no. 1–4, pp. 259–268, Nov. 1992. DOI: https://doi.org/10.1016/0167-2789(92)90242-F
P. Perona and J. Malik, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 12, no. 7, pp. 629–639, Jul. 1990. DOI: https://doi.org/10.1109/34.56205
Zhou Wang and A. C. Bovik, “Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures,” IEEE Signal Process. Mag., vol. 26, no. 1, pp. 98–117, Jan. 2009. DOI: https://doi.org/10.1109/MSP.2008.930649
A. Tanchenko, “Visual-PSNR measure of image quality,” J. Vis. Commun. Image Represent., vol. 25, no. 5, pp. 874–878, Jul. 2014. DOI: https://doi.org/10.1016/j.jvcir.2014.01.008
M. Goyal, Y. Lather, and V. Lather, “ANALYTICAL RELATION & COMPARISON OF PSNR AND SSIM ON BABBON IMAGE AND HUMAN EYE PERCEPTION USING MATLAB,” Int. J. Adv. Res. Eng. Appl. Sci. Impact Factor 5, vol. 795, no. 5, 2015.
B. J. Maiseli, O. A. Elisha, and H. Gao, “A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer,” EURASIP J. Image Video Process., vol. 2015, no. 1, p. 22, Dec. 2015.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Visibility to Structural Similarity,” 2004. DOI: https://doi.org/10.1109/TIP.2003.819861
P. K. Lavanya, “Performance evaluation of the various edge detectors and filters for the noisy IR images,” pp. 199–203.
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