Recovering Skin Reflectance and Geometry for Diagnosis of Melanoma

Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

In order to achieve an early detection of skin cancers, various state-of-the-art imaging modalities have been investigated from optical, impedance, biomechanical and physiological perspectives to find out the potential biomarkers. However multilayered skin microstructure and a wide spectrum of dynamical chromophores embedded underneath skin make it very challenging to quantify this mostly accessible, but very complex and heterogeneous largest organ of the human body. Rather than concentrating on characterizing those internal features in a microscopic level, both lesion reflectance (colour) and 3D geometry have been suggested to recover through a relative easy and cost effective way towards an improved diagnosis of melanoma. The reflectance recovered can be used as a good replacement for conventional photograph for the measurement of the ABCD criteria, while the geometry of lesion surface provides extra dimension for characterizing the topography disruption of lesion region. As both the reflectance and geometry of skin surface generally reflect the growth of chromophore cells under the skin, any external abnormalities indicating the change of skin conditions must accompany with some irregular evolvement and change with these cells. For example, a blurred and asymmetrical border possibly reveals an abnormal growth of melanocytes in the horizontal phase; whiles 3D surface indentations and protrusions accompanying variegated pigmentation may indicate an aggressive penetration of melanin into the dermal layer. We compared new features derived from reflectance and geometrical information with those traditional ones and demonstrated their significance as additional clues for melanoma diagnosis.

Keywords

Melanoma Skin surface Reflectance Profile Chromophore index Skin analyzer Topographic disruption Photometric stereo Segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Machine Vision LabUniversity of the West of EnglandBristolUK

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