HAIR ARTIFACT REMOVAL AND SKIN LESION SEGMENTATION OF DERMOSCOPY IMAGES

Julie Ann Salido; Conrado Ruiz Jr

doi:10.22159/ajpcr.2018.v11s3.30025

Authors

Julie Ann Salido Department of Software Technology, De La Salle University, College of Computer Studies, Manila, Philippines.
Conrado Ruiz Jr Department of Software Technology, De La Salle University, College of Computer Studies, Manila, Philippines.

DOI:

https://doi.org/10.22159/ajpcr.2018.v11s3.30025

Keywords:

Artifacts removal, Border detection, Dermoscopy image, Lesion segmentation

Abstract

Objective: The objective of this research is to perform automatic hair artifact removal and skin lesion segmentation on dermoscopy images.

Methods: Dermoscopy images are images from the examination of the skin lesion using a dermatoscope. There are different types of skin lesion artifacts, structures, or objects that are present in dermoscopy images. This is a pertinent problem that can inhibit the proper examination and accurately segment the skin lesion from the surrounding skin area. Artifacts, such as hair strands, introduce additional features that can also cause problems during classification. Our process starts with hair removal using a median filter on each color space of RGB, a bottom hat filter, a binary conversion, a dilation and morphological opening, and then the removal of small connected pixels. The detected hair regions are then filled up using harmonic inpainting. Then, skin lesion segmentation is performed using a binary conversion, a dilation, a perimeter detection and morphological opening, and then the removal of small connected pixels.

Results: Experiments were carried out on the PH2 dermoscopy images. The border of the lesion was quantified for evaluation by four statistical metrics with the lesions identified by the PH2 as the reference image, resulting with a true detection rate (TDR) of 82.31 and a false detection rate of 5.69.

Conclusions: The results obtained in the research work on hair artifacts removal and skin lesion segmentation provides acceptable results in terms of TDR and low false-positive rates.

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References

Taylor SC. Skin of color: Biology, structure, function, and implications for dermatologic disease. J Am Acad Dermatol 2002;46:S41-62.

Feldman SR, Fleischer AB Jr. McConnell RC. Most common dermatologic problems identified by internists, 1990-1994. Arch Intern Med 1998;158:726-30.

Papamichail M, Nikolaidis I, Nikolaidis N, Glava C, Lentzas I, Marmagkiolis K, et al. Merkel cell carcinoma of the upper extremity: Case report and an update. World J Surg Oncol 2008;6:32.

Sneyd MJ, Cox B. Melanoma in Maori, Asian, and pacific peoples in New Zealand. Cancer Epidemiol Biomarkers Prev 2009;18:1706-13.

Skin Cancer Foundation. Available From: http://www.skincancer.org/ skin-cancer-information/melanoma. [Last retrieved on 2017 Jul 06].

Johr RH. Dermoscopy: Alternative melanocytic algorithms-the ABCD rule of dermatoscopy, Menzies scoring method, and 7-point checklist. Clin Dermatol 2002;20:240-7.

Argenziano G, Zalaudek I, Corona R, Sera F, Cicale L, Petrillo G, et al. Vascular structures in skin tumors: A dermoscopy study. Arch Dermatol 2004;140:1485-9.

Pehamberger H, Steiner A, Wolff K. In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions. J Am Acad Dermatol 1987;17:571-83.

Nachbar F, Stolz W, Merkle T, Cognetta AB, Vogt T, Landthaler M, et al. The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions. J Am Acad Dermatol 1994;30:551-9.

Abbasi NR, Shaw HM, Rigel DS, Friedman RJ, McCarthy WH, Osman I, et al. Early diagnosis of cutaneous melanoma: Revisiting the ABCD criteria. JAMA 2004;292:2771-6.

Menzies AM, Haydu LE, Visintin L, Carlino MS, Howle JR, Thompson JF, et al. Distinguishing clinicopathologic features of patients with V600E and V600K BRAF-mutant metastatic melanoma. Clin Cancer Res 2012;18:3242-9.

Di Leo G, Fabbrocini G, Liguori C, Pietrosanto A, Sclavenzi M. In: Kayafas E, Loumos V, editors. ELM Image Processing for Melanocytic Skin Lesion Diagnosis Based on 7-Point Checklist: A Preliminary Discussion. Budapest, Hungary: IMEKO; 2004. p. 474-9.

Marghoob AA,Scope A, The complexity of diagnosing melanoma. J Invest Dermatol 2009;129:11-13.

Abbas Q, Celebi ME, Garca IF. Hair removal methods: A comparative study for dermoscopy images. Biomed Signal Proc Control 2011;6:395-404.

Celebi ME, Iyatomi H, Schaefer G, Stoecker WV. Lesion border detection in dermoscopy images. Comput Med Imaging Graph 2009;33:148-53.

Erkol B, Moss RH, Stanley RJ, Stoecker WV, Hvatum E. Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res Technol 2005;11:17-26.

Sonka M, Hlavac V, Boyle R. Image Processing, Analysis, and Machine Vision. US: Cengage Learning; 2014.

Bertalmio M, Sapiro G, Caselles V, Ballester C. Image in painting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques. US: ACM Press/Addison-Wesley Publishing Co; 2000. p. 417-24.

Drori I, Cohen-Or D, Yeshurun H. Fragment-based image completion. ACM Transact Grap 2003;22:303-12.

Shen J, Jin X, Zhou C. Gradient based image completion by solving poisson equation. Adv Multimedia Informat Proc 2005;2005:257-68.

Chan TF, Shen J. Nontexture inpainting by curvaturedriven diffusions. J Visual Communicat Image Represent 2001;12:436-49.

Barata C, Ruela M. Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Syst J 2013;99:1-15.

Silveira M, Nascimento J, Marques J, Maral A, Mendona T, Yamauchi S, et al. Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J Sel Top Signal Proc 2009;3:3545.

Mendonca T, Ferreira PM, Marques JS, Marcal AR, Rozeira J. PHÂ² - a dermoscopic image database for research and benchmarking. Conf Proc IEEE Eng Med Biol Soc 2013;2013:5437-40.

Parisotto S, Schnlieb C, MATLAB Codes for the Image Inpainting Problem, GitHub repository. MATLAB Central File Exchange; 2016.

Ying-Dong Q, Cheng-Song C, San-Ben C, Jin-Quan L. A fast subpixel edge detection method using Sobel Zernike moments operator. Image Vis Comput 2005;23:11-7.

Inc MathWorks, MATLAB: The Language of Technical Computing, Desktop Tools and Development Environment. USl MathWorks; 2005.

Celebi ME, Mendonca T, Marques JS. Dermoscopy Image Analysis. Boca Raton, FL: CRC Press; 2015.