The main challenge in an automated diagnostic system for the early diagnosis of melanoma is the correct segmentation
and classification of moles, often occluded by hair in images obtained with a dermoscope. Hair occlusion causes
segmentation algorithms to fail to identify the correct nevus border, and can cause errors in estimating texture measures.
We present a new method to identify hair in dermoscopic images using a universal approach, which can segment both
dark and light hair without prior knowledge of the hair type. First, the hair is amplified using a universal matched
filtering kernel, which generates strong responses for both dark and light hair without prejudice. Then we apply local
entropy thresholding on the response to get a raw binary hair mask. This hair mask is then refined and verified by a
model checker. The model checker includes a combination of image processing (morphological thinning and label
propagation) and mathematical (Gaussian curve fitting) techniques. The result is a clean hair mask which can be used to
segment and disocclude the hair in the image, preparing it for further segmentation and analysis. Application on real
dermoscopic images yields good results for thick hair of varying colours, from light to dark. The algorithm also performs
well on skin images with a mixture of both dark and light hair, which was not previously possible with previous hair
segmentation algorithms.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.