Abstract
The paper focuses on solving the problem of hair removal in dermatology applications. The proposed hair removal algorithm is based on Gabor filtering and PDE-based image reconstruction. It also includes the edge sharpening stage using a new warping algorithm. The idea of warping is to move pixels from the neighborhood of the blurred edge closer to the edge. The proposed technique preserves the overall luminosity and textures of the image, while making the edges sharper and less noisy.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Feature-preserving artifact removal from dermoscopy images. In: Proc. SPIE Med. Imaging, pp. 1–9 (2008)
Abbas, Q., Celebi, M.E., Garcia, I.F.: Hair removal methods: a comparative study for dermoscopy images. Biomed Signal Process Control 6, 395–404 (2011)
Abbas, Q., Fondon, I., Rashid, M.: Unsupervised skin lesions border detection via two-dimensional image analysis. Computer Methods and Programs in Biomedicine 27(1), 65–78 (2010)
Abbas, Q., Garcia, I.F., Celebi, M.E., Ahmad, W.: A feature-preserving hair removal algorithm for dermoscopy images. Skin Research and Technology 19(1), 27–36 (2013)
Abbas, Q., Garcia, I.F., Rashid, M.: Automatic skin tumor border detection for digital dermoscopy using a novel digital image analysis scheme. British Journal of Biomedical Science 67, 177–183 (2010)
Almeida, M., Figueiredo, M.: Parameter estimation for blind and non-blind deblurring using residual whiteness measures. IEEE Trans. Image Processing 22, 2751–2763 (2013)
Arad, N., Gotsman, C.: Enhancement by image-dependent warping. IEEE Trans. Image Proc. 8, 1063–1074 (1999)
Babacan, S.D., Molina, R., Katsaggelos, A.K.: Variational bayesian blind deconvolution using a total variation prior. IEEE Trans. Image Process. 18, 12–26 (2009)
Barcelos, C., Pires, V.: An automatic based nonlinear diffusion equations scheme for skin lesion segmentation. Appl. Math. Comput. 215, 251–261 (2009)
Bornemann, F., Marz, T.: Fast image inpainting based on coherence transport. Journal of Mathematical Imaging and Vision 28, 259–278 (2007)
Canny, J.: A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 679–714 (1986)
Chernomorets, A.A., Nasonov, A.V.: Deblurring in fundus images. In: 22nd Int. Conf. GraphiCon 2012, Moscow, Russia, pp. 76–79 (2012)
Chung, D., Sapiro, G.: Segmentation skin lesions with partial-differentialequation-based image processing algorithms. IEEE Trans. Med. Imaging 19, 763–767 (2000)
Criminisi, A., Perez, P., Toyama, K.: Region filling and object removal by exemplarbased image inpainting. IEEE Trans. Image Process. 13, 1–13 (2004)
Hu, H., de Haan, G.: Low cost robust blur estimator. In: IEEE International Conference on Image Processing, pp. 617–620 (2006)
Prades-Nebot, J., et al.: Image enhancement using warping technique. IEEE Electronics Letters 39, 32–33 (2003)
Krylov, A.S., Nasonov, A.V.: Adaptive image deblurring with ringing control. In: Fifth International Conference on Image and Graphics (ICIG 2009), pp. 72–75 (2009)
Lee, T., Ng, V., Gallagher, R., Coldman, A., McLean, D., Dullrazor, A.: Software approach to hair removal from images. J. Comput. Biol. Med. 27, 533–543 (1997)
Nagy, J.G., Palmer, K., Perrone, L.: Iterative methods for image deblurring: A matlab object-oriented approach. Numerical Algorithms 36(1), 73–93 (2004)
Nasonova, A.A., Krylov, A.S.: Determination of image edge width by unsharp masking. Computational Mathematics and Modelling 25, 72–78 (2014)
Nguyen, N., Lee, T., Atkinsa, 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)
Oliveira, J., Bioucas-Dia, J.M., Figueiredo, M.: Adaptive total variation image deblurring: A majorization-minimization approach. Signal Process. 89, 1683–1693 (2009)
Saugeona, P.S., Guillodb, J., Thiran, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Comput. Med. Imag. Grap. 27, 65–78 (2003)
Schmid, P.: Segmentation of digitized dermatoscopic images by twodimensional color clustering. IEEE Trans. Med. Imaging 18, 164–171 (1999)
Wighton, P., Lee, T., Atkinsa, M.: Dermoscopic hair disocclusion using inpainting. In: Proc. SPIE Med. Imaging, pp. 1–8 (2008)
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. Imag. Grap. 33, 275–282 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Nasonova, A., Nasonov, A., Krylov, A., Pechenko, I., Umnov, A., Makhneva, N. (2014). Image Warping in Dermatological Image Hair Removal. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-11755-3_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11754-6
Online ISBN: 978-3-319-11755-3
eBook Packages: Computer ScienceComputer Science (R0)