Simulation and analysis of optical skin lesion images.
OBJECTIVE
In order to properly analyse the effectiveness of methods for optically differentiating malignant from benign skin lesions, it is necessary to have a set of images for which the ground truth is known. However, aspects of the ground truth of clinical images such as true lesion boundary position are unknown or not known precisely. Therefore, a skin/lesion image simulation with known features including boundary location, skin pattern and lesion colour is needed to enable accurate assessment of feature estimation algorithms for lesion classification.


METHODS
In this paper, monochrome and colour skin/lesion images are synthesised with known characteristics including boundary, colour and skin pattern. Skin pattern is simulated with segmented lines with variations in length, orientation and intensity. Skin and lesion textures are modelled by an auto-regressive (AR) process. Monochrome skin lesion images are obtained by combining monochrome skin and lesion textures under the control of a known lesion shape with the addition of skin pattern. Colour skin lesion images are generated by mixing coloured skin and lesion textures. Finally, an inflammation area and image artefacts such as hair and specular reflection are added.


RESULTS
The synthesised images provide the image set for evaluating image pre-processing, segmentation and skin pattern analysis. The pre-processing includes hair removal and specular reflection reduction. An AR model interpolation is suggested for hair removal, and multiple illumination processing is developed to decrease specular reflection. A fast snake algorithm is extended to detect the boundaries of skin lesion and inflammation areas. Skin line direction is detected as a feature to measure the disruption of skin pattern caused by lesion.


CONCLUSIONS
Simulation of monochrome and colour skin/lesion image has been investigated, which is an alternative way to provide image set with known characteristics to validate image processing algorithms for image pre-processing, lesion/inflammation boundary detection and skin pattern analysis.
She
Zhishun
Z
Faculty of Technology & Computer Science, NEWI, Wrexham LL11 2AW, UK. z.she@newi.ac.uk
Duller
A W G
AW
Liu
Y
Y
Fish
P J
PJ
D003124:Colorimetry / Q000379:methods*; D003198:Computer Simulation; D003872:Dermatitis / Q000175:diagnosis; D046169:Dermoscopy / Q000379:methods*; D003937:Diagnosis, Differential; D004890:Erythema / Q000175:diagnosis / Q000503:physiopathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D019047:Phantoms, Imaging; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000175:diagnosis* / Q000503:physiopathology