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Skin_Hair Dataset: Setting the Benchmark for Effective Hair Inpainting Methods for Improving the Image Quality of Dermoscopic Images

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13804))

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Abstract

Dermoscopic images are often contaminated by artifacts including clinical pen markings, immersion fluid air bubbles, dark corners, and most importantly hair, which makes interpreting them more challenging for clinicians and computer-aided diagnostic algorithms. Hence, automated artifact recognition and inpainting systems have the potential to aid the clinical workflow as well as serve as an preprocessing step in the automated classification of dermoscopic images. In this paper, we share the first release of a public dermoscopic image dataset with hair artifacts which can be accessed here https://skin-hairdataset.github.io/SHD/. The Skin_Hair dataset contains over 252 dermoscopic images including artificial hair and will be expanded over time. Furthermore, we present the primary results of applying machine learning algorithms and GAN based architectures to the hair inpainting problem in dermoscopic images. We envision that these results will serve as a benchmark for researchers who might work on the hair detection and reconstruction tasks with this dataset in the future. In this work, we present a skin lesion image dataset based on the ISIC dataset containing dermoscopic images, images containing artificial hairs and the corresponding ground-truth masks. Furthermore, we use four hair inpainting methods including Navier-Stokes, Telea, Hair_SinGAN and R-MNet architectures which we evaluate using image quality assessment metrics MSE, PSNR, UQI and SSIM. The R-MNet architecture achieved the highest SSIM score of 0.960.

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References

  1. Abbas, Q., Celebi, M., García, I.F.: Hair removal methods: a comparative study for dermoscopy images. Biomed. Signal Process. Control 6(4), 395–404 (2011). https://doi.org/10.1016/j.bspc.2011.01.003, https://www.sciencedirect.com/science/article/pii/S1746809411000048

  2. Adegun, A.A., Viriri, S.: Deep learning-based system for automatic melanoma detection. IEEE Access 8, 7160–7172 (2020)

    Article  Google Scholar 

  3. Adegun, A.A., Viriri, S.: Deep learning techniques for skin lesion analysis and melanoma cancer detection: a survey of state-of-the-art. Artif. Intell. Rev. 54, 811–841 (2020)

    Article  Google Scholar 

  4. Almaraz-Damian, J.A., Ponomaryov, V., Sadovnychiy, S., Castillejos-Fernandez, H.: Melanoma and nevus skin lesion classification using handcraft and deep learning feature fusion via mutual information measures. Entropy 22(4) (2020). https://doi.org/10.3390/e22040484, https://www.mdpi.com/1099-4300/22/4/484

  5. Barbosa, J., Baleiras, M.: Melanoma detection using deep learning methods (2019)

    Google Scholar 

  6. Bardou, D., Bouaziz, H., Lv, L., Zhang, T.: Hair removal in dermoscopy images using variational autoencoders. Skin Res. Technol. 28(3), 445–454 (2022). https://doi.org/10.1111/srt.13145, https://onlinelibrary.wiley.com/doi/abs/10.1111/srt.13145

  7. Bertalmio, M., Bertozzi, A.L., Sapiro, G.: Navier-stokes, fluid dynamics, and image and video inpainting. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-I. IEEE (2001)

    Google Scholar 

  8. Bisla, D., Choromanska, A., Berman, R., Stein, J., Polsky, D.: Towards automated melanoma detection with deep learning: data purification and augmentation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2720–2728 (2019)

    Google Scholar 

  9. Bisla, D., Choromanska, A., Stein, J., Polsky, D., Berman, R.: Skin lesion segmentation and classification with deep learning system. arXiv:abs/1902.06061 (2019)

  10. Borys, D., Kowalska, P., Frackiewicz, M., Ostrowski, Z.: A simple hair removal algorithm from dermoscopic images. In: Ortuño, F., Rojas, I. (eds.) IWBBIO 2015. LNCS, vol. 9043, pp. 262–273. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16483-0_27

    Chapter  Google Scholar 

  11. Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., Yap, M.H.: Analysis of the ISIC image datasets: usage, benchmarks and recommendations. Med. Image Anal. 75, 102305 (2022)

    Article  Google Scholar 

  12. Celebi, M.E., Barata, C., Halpern, A., Tschandl, P., Combalia, M., Liu, Y.: Guest editorial: image analysis in dermatology. Med. Image Anal. 79, 102468 (2022). https://doi.org/10.1016/j.media.2022.102468

    Article  Google Scholar 

  13. Chan, T.F., Shen, J.: Nontexture inpainting by curvature-driven diffusions. J. Visual Commun. Image Represent. 12(4), 436–449 (2001). https://doi.org/10.1006/jvci.2001.0487, https://www.sciencedirect.com/science/article/pii/S1047320301904870

  14. Codella, N., et al.: Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration (ISIC) (2018)

    Google Scholar 

  15. Codella, N.C., et al.: Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC). In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp. 168–172. IEEE (2018)

    Google Scholar 

  16. Combalia, M., et al.: Bcn20000: Dermoscopic lesions in the wild (2019)

    Google Scholar 

  17. Fiorese, M., Peserico, E., Silletti, A.: Virtualshave: automated hair removal from digital dermatoscopic images. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5145–5148 (2011). https://doi.org/10.1109/IEMBS.2011.6091274

  18. Gutman, D., et al.: Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, Hosted by the International Skin Imaging Collaboration (ISIC) (2016)

    Google Scholar 

  19. Horé, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, pp. 2366–2369 (2010). https://doi.org/10.1109/ICPR.2010.579

  20. Huang, A., Kwan, S.Y., Chang, W.Y., Liu, M.Y., Chi, M.H., Chen, G.S.: A robust hair segmentation and removal approach for clinical images of skin lesions. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3315–3318 (2013). https://doi.org/10.1109/EMBC.2013.6610250

  21. ISIC: Isic archive gallery. Online, July 2020. https://www.isic-archive.com

  22. Jam, J., Kendrick, C., Drouard, V., Walker, K., Hsu, G.S., Yap, M.H.: R-MNET: a perceptual adversarial network for image inpainting. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2714–2723 (2021)

    Google Scholar 

  23. Kassem, M.A., Hosny, K.M., Fouad, M.M.: Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access 8, 114822–114832 (2020). https://doi.org/10.1109/ACCESS.2020.3003890

    Article  Google Scholar 

  24. Kiani, K., Sharafat, A.R.: E-shaver: an improved dullrazor® for digitally removing dark and light-colored hairs in dermoscopic images. Comput. Biol. Med. 41(3), 139–145 (2011). https://doi.org/10.1016/j.compbiomed.2011.01.003, https://www.sciencedirect.com/science/article/pii/S0010482511000047

  25. Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D.: Dullrazor®: A software approach to hair removal from images. Comput. Biol. Med. 27(6), 533–543 (1997). https://doi.org/10.1016/S0010-4825(97)00020-6, https://www.sciencedirect.com/science/article/pii/S0010482597000206

  26. Li, W., Joseph Raj, A.N., Tjahjadi, T., Zhuang, Z.: Digital hair removal by deep learning for skin lesion segmentation. Pattern Recogn. 117, 107994 (2021). https://doi.org/10.1016/j.patcog.2021.107994, https://www.sciencedirect.com/science/article/pii/S0031320321001813

  27. Maglogiannis, I., Delibasis, K.: Hair removal on dermoscopy images. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2960–2963 (2015). https://doi.org/10.1109/EMBC.2015.7319013

  28. Maron, R.C., et al.: Reducing the impact of confounding factors on skin cancer classification via image segmentation: technical model study. J. Med. Internet Res. 23 (2021)

    Google Scholar 

  29. Nauta, M., Walsh, R., Dubowski, A., Seifert, C.: Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis. Diagnostics 12(1) (2022). https://doi.org/10.3390/diagnostics12010040, https://www.mdpi.com/2075-4418/12/1/40

  30. Pewton, S.W., Yap, M.H.: Dark corner on skin lesion image dataset: does it matter? In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 4831–4839, June 2022

    Google Scholar 

  31. Rotemberg, V., et al.: A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8, 34 (2021). https://doi.org/10.1038/s41597-021-00815-z

  32. Rott Shaham, T., Dekel, T., Michaeli, T.: SinGAN: Learning a generative model from a single natural image. In: IEEE International Conference on Computer Vision (ICCV)(2019)

    Google Scholar 

  33. Salido, J.A.A., Ruiz, C.: Using morphological operators and inpainting for hair removal in dermoscopic images. In: Proceedings of the Computer Graphics International Conference. CGI 2017, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3095140.3095142, https://doi.org/10.1145/3095140.3095142

  34. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. Natl. Acad. Sci. 93(4), 1591–1595 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  35. Shaham, T.R., Dekel, T., Michaeli, T.: SinGAN: learning a generative model from a single natural image. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4570–4580 (2019)

    Google Scholar 

  36. Song, X., et al.: Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization criminisi algorithm. Biomed. Signal Process. Control 78, 103967 (2022). https://doi.org/10.1016/j.bspc.2022.103967, https://www.sciencedirect.com/science/article/pii/S1746809422004669

  37. Sultana, A., Dumitrache, I., Vocurek, M., Ciuc, M.: Removal of artifacts from dermatoscopic images. In: 2014 10th International Conference on Communications (COMM), pp. 1–4 (2014). https://doi.org/10.1109/ICComm.2014.6866757

  38. Talavera-Martínez, L., Bibiloni, P., González-Hidalgo, M.: Comparative study of dermoscopic hair removal methods. In: Tavares, J.M.R.S., Natal Jorge, R.M. (eds.) VipIMAGE 2019. LNCVB, vol. 34, pp. 12–21. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32040-9_2

    Chapter  Google Scholar 

  39. Telea, A.: An image inpainting technique based on the fast marching method. J. Graphics Tools 9(1), 23–34 (2004)

    Article  Google Scholar 

  40. Toossi, M.T.B., Pourreza, H.R., Zare, H., Sigari, M.H., Layegh, P., Azimi, A.: An effective hair removal algorithm for dermoscopy images. Skin Res. Technol. 19 (2013)

    Google Scholar 

  41. Tschandl, P.: The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions (2018). https://doi.org/10.7910/DVN/DBW86T, https://doi.org/10.7910/DVN/DBW86T

  42. Wang, Z., Bovik, A.C.: Mean squared error: love it or leave it? A new look at signal fidelity measures. IEEE Signal Process. Mag. 26(1), 98–117 (2009). https://doi.org/10.1109/MSP.2008.930649

    Article  Google Scholar 

  43. Xie, F.Y., Qin, S.Y., Jiang, Z.G., Meng, R.S.: PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Comput. Med. Imaging Graph. 33(4), 275–282 (2009). https://doi.org/10.1016/j.compmedimag.2009.01.003, https://www.sciencedirect.com/science/article/pii/S0895611109000056

  44. Xie, Y., Zhang, J., Xia, Y., Shen, C.: A mutual bootstrapping model for automated skin lesion segmentation and classification. IEEE Trans. Med. Imaging 39, 2482–2493 (2020)

    Article  Google Scholar 

  45. Zanddizari, H., Nguyen, N., Zeinali, B., Chang, J.M.: A new preprocessing approach to improve the performance of CNN-based skin lesion classification. Med. Biol. Eng. Comput. 59(5), 1123–1131 (2021). https://doi.org/10.1007/s11517-021-02355-5

    Article  Google Scholar 

  46. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

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Acknowledgments

We gratefully acknowledge the funding support of the research project by the program “Excellence initiative—research university” for the AGH UST and the NAWA Bekker Scholarship for J. Jaworek-Korjakowska.

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Authors and Affiliations

  1. AGH University of Science and Technology, Al Mickiewicza 30, 30-059, Krakow, Poland

    Joanna Jaworek-Korjakowska, Anna Wojcicka, Dariusz Kucharski & Andrzej Brodzicki

  2. Department of Pathology, Stanford School of Medicine, 300 Pasteur Drive, Stanford, CA, 94305-5324, USA

    Joanna Jaworek-Korjakowska

  3. Manchester Metropolitan University, John Dalton Building, Chester Street, Manchester, M1 5GD, UK

    Connah Kendrick, Bill Cassidy & Moi Hoon Yap

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  1. Joanna Jaworek-Korjakowska
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Correspondence to Joanna Jaworek-Korjakowska .

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Editors and Affiliations

  1. IBM Research - MIT-IBM Watson AI Lab, Massachusetts, USA

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Jaworek-Korjakowska, J. et al. (2023). Skin_Hair Dataset: Setting the Benchmark for Effective Hair Inpainting Methods for Improving the Image Quality of Dermoscopic Images. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_12

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