Skin Hair Removal in Dermoscopic Images Using Soft Color Morphology

Dermoscopic images are useful tools towards the diagnosis and classification of skin lesions. One of the first steps to automatically study them is the reduction of noise, which includes bubbles caused by the immersion fluid and skin hair. In this work we provide an effective hair removal algorithm for dermoscopic imagery employing soft color morphology operators able to cope with color images. Our hair removal filter is essentially composed of a morphological curvilinear object detector and a morphological-based inpainting algorithm. Our work is aimed at fulfilling two goals. First, to provide a successful yet efficient hair removal algorithm using the soft color morphology operators. Second, to compare it with other state-of-the-art algorithms and exhibit the good results of our approach, which maintains lesion’s features.

[1]  T Lee,et al.  Dullrazor®: A software approach to hair removal from images , 1997, Comput. Biol. Medicine.

[2]  Hamid Reza Pourreza,et al.  An effective hair removal algorithm for dermoscopy images , 2013, Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging.

[3]  Manuel González Hidalgo,et al.  Soft color morphology , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[4]  Michal Baczynski,et al.  Fuzzy Implications , 2008, Studies in Fuzziness and Soft Computing.

[5]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[6]  I Zalaudek,et al.  Blue‐black rule: a simple dermoscopic clue to recognize pigmented nodular melanoma , 2011, The British journal of dermatology.

[7]  Pedro M. Ferreira,et al.  PH2 - A dermoscopic image database for research and benchmarking , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[8]  Manuel González Hidalgo,et al.  A Fuzzy Filter for High-Density Salt and Pepper Noise Removal , 2013, CAEPIA.

[9]  Etienne Kerre,et al.  Fuzzy techniques in image processing , 2000 .

[10]  Mariano Eriz Aggregation Functions: A Guide for Practitioners , 2010 .

[11]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.