Intrinsic melanin and hemoglobin colour components for skin lesion malignancy detection

Authors:
Ali Madooei;Mark S. Drew;Maryam Sadeghi;M. Stella Atkins
Affiliations:
School of Computing Science, Simon Fraser University, Canada;School of Computing Science, Simon Fraser University, Canada;School of Computing Science, Simon Fraser University, Canada;School of Computing Science, Simon Fraser University, Canada
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

Citing 4
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Abstract

In this paper we propose a new log-chromaticity 2-D colour space, an extension of previous approaches, which succeeds in removing confounding factors from dermoscopic images: (i) the effects of the particular camera characteristics for the camera system used in forming RGB images; (ii) the colour of the light used in the dermoscope; (iii) shading induced by imaging non-flat skin surfaces; (iv) and light intensity, removing the effect of light-intensity falloff toward the edges of the dermoscopic image. In the context of a blind source separation of the underlying colour, we arrive at intrinsic melanin and hemoglobin images, whose properties are then used in supervised learning to achieve excellent malignant vs. benign skin lesion classification. In addition, we propose using the geometric-mean of colour for skin lesion segmentation based on simple grey-level thresholding, with results outperforming the state of the art.