Algorithmic reproduction of asymmetry and border cut-off parameters according to the ABCD rule for dermoscopy.

BACKGROUND: Semiquantitative algorithms were applied to dermoscopic images to improve the clinical diagnosis for melanoma.
OBJECTIVE: The aim of the study was to develop a computerized method for automated quantification of the 'A' (asymmetry) and 'B' (border cut-off) parameters, according to the ABCD rule for dermoscopy, thus reproducing human evaluation.
METHODS: Three hundred and thirty-one melanocytic lesion images, referring to 113 melanomas and 218 melanocytic nevi, acquired by means of a digital videodermatoscope, were considered. Images were evaluated by two experienced observers and by using computer algorithms developed by us. Clinical evaluation of asymmetry was performed by attributing scores to shape asymmetry and asymmetry of pigment distribution and structures, whereas computer evaluation of shape and pigment distribution asymmetries were based on the assessment of differences in area and lightness in the two halves of the image, respectively. Borders were evaluated both by clinicians and by the computer, by attributing a score to each border segment ending abruptly. Differences between nevus and melanoma values were evaluated using the chi-square test, while Cohen's Kappa index for agreement was employed for the evaluation of the concordance between human and computer.
RESULTS: Pigment distribution asymmetry appears the most striking parameter for melanoma diagnosis both for human and for automated diagnosis. A good concordance between clinicians and computer evaluation was achieved for all asymmetry parameters, and was excellent for border cut-off evaluation.
CONCLUSIONS: These algorithms enable a good reproduction of the 'A' and 'B' parameters of the ABCD rule for dermoscopy, and appear useful for diagnostic and learning purposes.