Boundary Structural irregularity of skin lesions has shown the most significance in the early diagnosis of melanomas in clinic. Aiming to measure structural irregularities of skin lesion contours and designing feature oriented classifiers

a method for extracting structural components of contours based on wavelet sub-band analysis and neural network classifiers using multi-scale features is proposed in this paper. A set of salient wavelet sub-bands are searched using Hausdorff distance evaluations on energy at wavelet subbands. Then structural components of skin lesion contours are reconstructed. Furthermore

multi-scale descriptions of the contours structural irregularity are given using statistical and geometrical measures based on the salient wavelet sub-bands. Finally

various experimental schemes are taken on single scale/multi-scale features

for small training sets and large training sets as well as and before and after feature selection for a comparison on performances of the neural network classifiers. It is shown from experiments that the classifiers sensitivity and specialty after feature selection using small sample sets equal and exceed that of large training sets respectively as dimensions of feature spaces are expanded by the proposed multi-scale independent features at salient wavelet sub-bands.