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Dermatol Res Skin Care 2017 | Volume 1 Issue 1
World
Dermatologist Summit and Skin Care Expo
October 30-31, 2017 | Toronto, Canada
Dermoscopy is a noninvasive diagnostic technique which
is useful in diagnosis of many skin diseases. In recent
years, dermoscopy technology has been developing towards
network platforms, and more non-clinical physicians have
chance to capture and upload dermoscopy images into
remote diagnosis systems. Unfortunately, this process can
easily lead to poor image quality (arising from for example
hair, blur and uneven illumination) which can adversely
influence consequent automatic image analysis results
on potential lesion objects. The purpose of this study is
to deploy an algorithm that can automatically assess the
quality of dermoscopy images. Such an algorithm could be
used to direct image recapture or correction. We describe
an application-driven No-Reference (NR) Image Quality
Assessment (IQA) model for dermoscopy images affected by
possibly multiple distortions. For this purpose, we created a
multiple distortion dataset of dermoscopy images impaired
by varying degrees of blur and uneven illumination. The basis
of this model is two single distortion IQA metrics that are
sensitive to blur and uneven illumination, respectively. The
outputs of these two metrics are combined to predict the
quality of multiply distorted dermoscopy images using a fuzzy
neural network. Unlike traditional IQA algorithms, which use
human subjective score as ground truth, here ground truth
is driven by the application, and generated according to the
degree of influence of the distortions on lesion analysis.
The experimental results reveal that the proposed model
delivers accurate and stable quality prediction results for
dermoscopy images impaired by multiple distortions.
e:
Application-driven no-reference quality assessment for dermoscopy images with multiple distortions
Fengying Xie
Beihang University, China