A new method describing border irregularity of pigmented lesions.
OBJECTIVE
Automatic quantitative characterization of border irregularity generating useful descriptors is a highly important task for computer-aided diagnosis of melanoma. This paper proposes a novel approach to describe the border irregularity of melanomas aiming at achieving higher recognition rates.


METHODS
By introducing a boundary characteristic description, which we call a centroid distance diagram (CDD), a compact-supported mapping, called the centroid distance curve, can be extracted from this diagram. The centroid distance curve establishes the projection from angular orientations to the sum of the lengths of those line segments connecting the lesion centroid and border points. Border irregularity descriptors generated from CDDs include the non-centroid-convexity index, the maximum-minimum distance indicator, the standard deviation of centroid distance curves and the maximum magnitude of non-zero frequency elements of centroid distance curves after discrete Fourier transforms. Upper limits of the error boundaries involved in these descriptors are estimated.


RESULTS
Experimental studies are based on 60 melanoma and 107 benign lesion images collected from local pigmented lesion clinics. By applying the proposed descriptors, receiver operating characteristic (ROC) curves are constructed by projecting the features into a linear space learned from samples. The optimal sensitivity and specificity for the proposed method are 74.2% and 72.6%. The total area enclosed by the corresponding ROC curve is 0.788. In addition, as the training and testing study for melanoma recognition in the literature is largely missing, a comprehensive comparative study is conducted by randomly dividing the data into two groups: one for training and one for testing. For the testing group, the best mean sensitivity obtained with the descriptors proposed in this paper reaches 71.8% and the standard deviation is 10.1%. The specificity for the testing group corresponding to the optimal sensitivity is 69.8%, with a standard deviation of 7.2%.


CONCLUSIONS
This study suggests that in terms of sensitivity, descriptors extracted from CDDs are the most powerful ones in characterizing the border irregularity of melanomas.
Zhou
Yu
Y
Machine Vision Laboratory, University of the West of England, Bristol, UK. yu.zhou@uwe.ac.uk
Smith
Melvyn
M
Smith
Lyndon
L
Warr
Robert
R
D046169:Dermoscopy / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005583:Fourier Analysis; D017709:Fractals; D006801:Humans; D016014:Linear Models; D008545:Melanoma / Q000473:pathology*; D008954:Models, Biological; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*