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BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access March 4, 2016

Effcient and Effective Automated Digital Hair Removal from Dermoscopy Images

  • Joost Koehoorn , André Sobiecki , Paulo Rauber , Andrei Jalba and Alexandru Telea


We propose a method for digital hair removal from dermoscopic images, based on a threshold-set model. For every threshold, we adapt a recent gap-detection algorithm to find hairs, and merge results in a single mask image.We find hairs in this mask by combining morphological filters and medial descriptors.We derive robust parameter values for our method from over 300 skin images.We detail a GPU implementation of our method and show how it compares favorably with five existing hair removal methods, in terms of removing both long and stubble hair of various colors, contrasts, and curvature. We also discuss qualitative and quantitative validations of the produced hair-free images, and show how our method effectively addresses the task of automatic skin-tumor segmentation for hair-occluded images.


[1] Abbas, Q., Fondon, I., Rashid, M.: Unsupervised skin lesions border detection via two-dimensional image analysis. Comp. Meth. Prog. Biom. 104, 1–15 (2011) Search in Google Scholar

[2] Abbas, Q., Celebi, M.E., García, I.F.: Hair removal methods: A comparative study for dermoscopy images. Biomed Signal Proc Control 6(4), 395–404 (2011) 10.1016/j.bspc.2011.01.003Search in Google Scholar

[3] Altman, N.: An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46(3), 175– 185 (1992) Search in Google Scholar

[4] Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proc. ACM SIGGRAPH. pp. 417–424 (2000) 10.1145/344779.344972Search in Google Scholar

[5] Bornemann, F., März, T.: Fast image inpainting based on coherence transport. J. Math. Imaging Vis 28, 259–278 (2007) 10.1007/s10851-007-0017-6Search in Google Scholar

[6] Cao, T., Tang, K., Mohamed, A., Tan, T.: Parallel banding algorithm to compute exact distance transform with the GPU. In: Proc. ACM I3D. pp. 83–90 (2010) 10.1145/1730804.1730818Search in Google Scholar

[7] Celebi, M., Kingravi, H., Uddin, B., Iyatomi, H., Aslandogan, A., Stoecker, W., Moss, R.: A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31(6), 362–373 (2007) 10.1016/j.compmedimag.2007.01.003Search in Google Scholar PubMed PubMed Central

[8] Christensen, J., Soerensen, M., Linghui, Z., Chen, S., Jensen, M.: Pre-diagnostic digital imaging prediction model to discriminate between malignant melanoma and benign pigmented skin lesion. Skin Res. Technol. 16 (2010) 10.1111/j.1600-0846.2009.00408.xSearch in Google Scholar PubMed

[9] Cokelaer, F., Talbot, H., Chanussot, J.: Efficient robust d-dimensional path operators. IEEE J. Selected Topics in Signal Processing 6(7), 830–839 (2012) 10.1109/JSTSP.2012.2213578Search in Google Scholar

[10] Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE TPAMI 24(5), 603–619 (2002) 10.1109/34.1000236Search in Google Scholar

[11] Cortes, C., Vapnik, V.: Support-vector networks. Mach Learn 20(3), 273–297 (1995) 10.1007/BF00994018Search in Google Scholar

[12] Couprie, M., Bezerra, F.N., Bertrand, G.: Topological operators for grayscale image processing. J. Electronic Imag. 10(4), 1003–1015 (2001) 10.1117/1.1408316Search in Google Scholar

[13] Fiorese, M., Peserico, E., Silletti, A.: VirtualShave: automated hair removal from digital dermatoscopic images. In: Proc. IEEE EMBS. pp. 5145–5148 (2011) 10.1109/IEMBS.2011.6091274Search in Google Scholar PubMed

[14] Flores, E., Scharcanski, J.: Segmentation of pigmented melanocytic skin lesions based on learned dictionaries and normalized graph cuts. In: Graphics, Patterns and Images (SIBGRAPI), 2014 27th SIBGRAPI Conference on. pp. 33–40 (Aug 2014) Search in Google Scholar

[15] FotoFinder: Handyscope mobile dermatoscope specifications (2015), Search in Google Scholar

[16] Huang, A., Kwan, S., Chang, W., Liu, M., Chi, M., Chen, G.: A robust hair segmentation and removal approach for clinical images of skin lesions. In: Proc. EMBS. pp. 3315–3318 (2013) 10.1109/EMBC.2013.6610250Search in Google Scholar PubMed

[17] Iyatomi, H., Oka, H., Celebi, G., Hashimoto, M., Hagiwara, M., Tanaka, M., Ogawa, K.: An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comp. Med. Imag. Graph. 32(7), 566–579 (2008) Search in Google Scholar

[18] Kiani, K., Sharafat, A.: E-shaver: An improved dullrazor for digitally removing dark and light-colored hairs in dermoscopic images. Comput Biol Med 41(3), 139–145 (2011) 10.1016/j.compbiomed.2011.01.003Search in Google Scholar PubMed

[19] Koehoorn, J., Sobiecki, A., Boda, D., Diaconeasa, A., Doshi, S., Paisey, S., Jalba, A., Telea, A.: Automated digital hair removal by threshold decomposition and morphological analysis. In: Proc. ISMM. pp. 324–335 (2015) 10.1007/978-3-319-18720-4_2Search in Google Scholar

[20] Koehoorn, J., Sobiecki, A., Boda, D., Diaconeasa, A., Jalba, A., Telea, A.: Digital hair removal source code (2014), www.cs. Search in Google Scholar

[21] Kohonen, T.: Learning vector quantization. In: Self-Organizing Maps, pp. 203–217. Springer (1997) 10.1007/978-3-642-97966-8_6Search in Google Scholar

[22] Korotkov, K., Garcia, R.: Methodological review: Computerized analysis of pigmented skin lesions: A review. Artif. Intell. Med. 56(2), 69–90 (2012) Search in Google Scholar

[23] Lee, H.Y., Lee, H.K., Kim, T., Park,W.: Towards knowledge-based extraction of roads from 1m-resolution satellite images. In: Proc. SSIAI. pp. 171–178 (2000) Search in Google Scholar

[24] 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) Search in Google Scholar

[25] Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE TPAMI 19(12), 3243–3254 (2010) Search in Google Scholar

[26] Nguyen, N., Lee, T., Atkins, M.: Segmentation of light and dark hair in dermoscopic images: a hybrid approach using a universal kernel. In: Proc. SPIE Med. Imaging. pp. 1–8 (2010) 10.1117/12.844572Search in Google Scholar

[27] Parolin, A., Herzer, E., Jung, C.: Semi-automated diagnosis of melanoma through the analysis of dermatological images. In: Proc. SIBGRAPI. pp. 71–78. IEEE Press (2010) 10.1109/SIBGRAPI.2010.18Search in Google Scholar

[28] Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE TPAMI 12(7), 629–639 (1990) 10.1109/34.56205Search in Google Scholar

[29] Rahimi, A.: Fast connected components on images. (2014) Search in Google Scholar

[30] Rauber, P., da Silva, R., Feringa, S., Celebi, M., Falcao, A., Telea, A.: Interactive image feature selection aided by dimensionality reduction. In: Proc. EuroVA. pp. 322–328 (2015) Search in Google Scholar

[31] Rauber, P., Falcao, A., Spina, T., De Rezende, P.: Interactive segmentation by image foresting transform on superpixel graphs. In: Graphics, Patterns and Images (SIBGRAPI), 2013 26th SIBGRAPI - Conference on. pp. 131–138 (Aug 2013) 10.1109/SIBGRAPI.2013.27Search in Google Scholar

[32] Saugeon, P., Guillod, J., Thiran, J.: Towards a computer-aided diagnosis system for pigmented skin lesions. Comput. Med. Imag. Grap. 27, 65–78 (2003) Search in Google Scholar

[33] Scharkanski, J., Celebi, M.: Computer Vision Techniques for the Diagnosis of Skin Cancer. Springer (2014) 10.1007/978-3-642-39608-3Search in Google Scholar

[34] Sobiecki, A., Jalba, A., Boda, D., Diaconeasa, A., Telea, A.: Gap-sensitive segmentation and restoration of digital images. In: Proc. EG GVC. pp. 136–144 (2014) Search in Google Scholar

[35] Telea, A.: Data Visualization – Principles and practice. CRC Press (2014), 2nd edition 10.1201/b17217Search in Google Scholar

[36] Telea, A.: An image inpainting technique based on the fast marching method. J. Graphics, GPU, & Game Tools 9(1), 23–34 (2004) 10.1080/10867651.2004.10487596Search in Google Scholar

[37] Telea, A., vanWijk, J.J.: An augmented fastmarching method for computing skeletons and centerlines. In: Proc. VisSym. pp. 251–259 (2002) Search in Google Scholar

[38] Wighton, P., Lee, T., Atkins, M.: Dermascopic hair disocclusion using inpainting. In: Proc. SPIE Med. Imaging. pp. 144–151 (2008) 10.1117/12.770776Search in Google Scholar

[39] Xie, F., Qin, S., Jiang, Z., Meng, R.: PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Comp. Med. Imag. Graph. 33(4), 275–282 (2009) Search in Google Scholar

[40] van der Zwan, M., Meiburg, Y., Telea, A.: A dense medial descriptor for image analysis. In: Proc. VISAPP. pp. 285–293 (2013) Search in Google Scholar

Received: 2015-6-25
Accepted: 2016-1-27
Published Online: 2016-3-4

© 2016 Joost Koehoorn et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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