(41-4) 09 * << * >> * Russian * English * Content * All Issues

 PDF 904 kB

DOI: 10.18287/2412-6179-2017-41-4-521-527

Pages: 521-527.

Abstract:
Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 non-overlapped blocks and for each block, white pixels are replaced by locating the highest peak, using a histogram function and a morphological close operation. The proposed algorithm reports a true positive rate (sensitivity) of 97.36 %, a false positive rate (fall-out) of 4.25 %, and a true negative rate (specificity) of 95.75 %. The diagnostic accuracy achieved is recorded at a high level of 95.78 %.

Keywords:
dermoscopy image, melanoma, hair detection, hair removal, inpainting, skin lesion.

Citation:
Zaqout IS. An efficient block-based algorithm for hair removal in dermoscopic images. Computer Optics 2017; 41(4): 521-527. DOI: 10.18287/2412-6179-2017-41-4-521-527.

1. Iyatomi H. Computer-based diagnosis of pigmented skin lesions. In book: Campolo D, ed. New developments in biomedical engineering. Chap 10. “In Tech” Publisher; 2010: 183-200. ISBN: 978-953-7619-57-2.
2. Garnavi R. Computer-aided diagnosis of melanoma. PhD thesis. Australia: “The University of Melbourne” Publisher; 2011.
3. American academy of dermatology (AAD). Basal cell carcinoma. Source: áhttp://www.aad.org/skin-conditions/derma­tology-a-to-z/skin-cancerñ.
4. Zaqout I. Diagnosis of skin lesions based on dermoscopic images using image processing techniques. International Journal of Signal Processing, Image Processing and Pattern Recognition 2016; 9(9): 189-204. DOI: 10.14257/ij­sip.2016.9.9.18.
5. PH2 Database. Source: áhttps://www.fc.up.pt/addi/ph2%20database.htmlñ.
6. Abbas Q, Celebi ME, Garcia IF. Hair removal methods: A comparative study for dermoscopy images. Biomedical Signal Process and Control 2011; 6(4): 395-404. DOI: 10.1016/j.bspc.2011.01.003.
7. Somnathe PA, Gumaste PP. A review of existing hair removal methods in dermoscopic images. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE) 2015; 1: 73-76.
8. Satheesha TY, Satyanarayana D, Giriprasad MN. A pixel interpolation technique for curved hair removal in skin images to support melanoma detection. Journal of Theoretical and Applied Information Technology 2014; 70(3): 559-565.
9. Nasonova A, Nasonov A, Krylov A, Pechenko I, Umnov A, Makhneva N. Image warping in dermatological image hair removal. Image Analysis and Recognition (ICIAR 2014): 159-166. DOI: 10.1007/978-3-319-11755-3_18.
10. Mahmood AF, Mahmood HA. Artifact removal from skin dermoscopy images to support automated melanoma diagnosis. Al-Rafadain Engineering Journal 2015; 23(5): 22-30.
11. Das P, Gangopadhyay M. Removal of hair particles from skin disease images using pixel based approach. International Journal of Computer Science and Information Technologies 2015; 6(5): 4154-4158.
12. Abbas Q, Garcia IF, Celebi ME, Ahmad W. A feature-preserving hair removal algorithm for dermoscopy images. Skin Research and Technology 2011; 19(1): e27-e36. DOI: 10.1111/j.1600-0846.2011.00603.x.
13. Mirzaalian H, Lee TK, Hamarneh G. Hair enhancement in dermoscopic images using dual-channel quaternion tubularness filters and MRF-based multi-label optimization. IEEE Transaction on Image Processing 2014; 23(12): 5486-5496. DOI: 10.1109/TIP.2014.2362054.
14. Kretzler M. Automated curved hair detection and removal in skin images to support automated melanoma detection. Case Western Reserve University; 2013.
15. Okuboyejo D, Olugbara O, Odunaike S. Unsupervised restoration of hair-occluded lesion in dermoscopic images. MIUA, Epham, UK, 2014; 91-96.
16. 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. 12th International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing ISMM 2015, Reykjavik, Iceland, 2015: 15-26. DOI: 10.1007/978-3-319-18720-4_2.
17. Koehoorn J, Sobiecki A, Rauber P, Jalba A, Telea A. Efficient and effective automated digital hair removal from dermoscopy images. Math Morphol Theory Appl 2016; 1(1): 1-17. DOI: 10.1515/mathm-2016-0001.
18. Zhou H, Chen M, Gass R, Rehg JM, Ferris L, Ho J, Drogowski L. Feature-preserving artifact removal from dermoscopy images. Proc SPIE 2008; 6914: 69141B. DOI: 10.1117/12.770824.
19. Hearn J. Competitive medical image segmentation with the fast marching method, Case Western Reserve University; 2008.
20. Sultana A, Ciuc M, Radulescu T, Wanyu L, Petrache D. Preliminary work on dermatoscopic lesion segmentation. Proceedings of the 20th European Signal Processing Conference (EUSIPCO) 2012; 2273-2277.
21. Toossi MT, Pourreza HR, Zare H, Sigari MH, Layegh P, Azimi A. An effective hair removal algorithm for dermoscopy images. Skin Research and Technology 2013; 19(3): 230-235. DOI: 10.1111/srt.12015.
22. Lee T, Ng V, Gallager R, Coldman A, McLean D. DullRazor®: A software approach to hair removal from images. Computers in Biology and Medicine 1997; 27(6): 533-543. DOI: 10.1016/S0010-4825(97)00020-6.
23. Kiani K, Sharafat AR. E-Shaver: An improved DullRazor® for digitally removing dark and light-colored hairs in dermoscopic images. Computers in Biology and Medicine 2011; 41(3): 139-145. DOI: 10.1016/j.compbio­med.2011.01.003.
24. Fiorese M, Peserico E, Silletti A. VirtualShave: automated hair removal from digital dermatoscopic images. 33rd Annual International Conference of the IEEE EMBS 2011; 5145-5148. DOI: 10.1109/IEMBS.2011.6091274.
25. Bertalmio M, Sapiro G, Caselles V, Ballester C. Image inpainting. Proceedings of the 27th annual conference on Computer graphics and interactive techniques (SIGGRAPH '00) 2000; 417-424. DOI: 10.1145/344779.344972.
26. 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. 35th Annual International Conference of the IEEE EMBS 2013; 3315-3318. DOI: 10.1109/EMBC.2013.6610250.
27. Xie FY, Qin SY, Jiang ZG, Meng RS. PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Computerized Medical Imaging and Graphics 2009; 33(4): 275-282. DOI: 10.1016/j.compmedimag.2009.01.003.
28. Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990; 12(7): 629-639. DOI: 10.1109/34.56205.
29. Bornemann F, März T. Fast image inpainting based on coherence transport. Journal of Mathematical Imaging and Vision 2007; 28(3): 259-278. DOI: 10.1007/s10851-007-0017-6.
30. Telea A. An image inpainting technique based on the fast marching method. Journal of Graphics Tools 2004; 9(1): 25-36. DOI: 10.1080/10867651.2004.10487596.