Measuring the contour boundary irregularities of skin lesion is an important factor in early detection of malignant melanoma. On the other hand, cancer is usually recognized as a chaotic growth of cells. It is generally assumed that boundary irregularity associated with biomedical images may be due to the chaotic behavior of its originated system. Thus, chaotic indices can serve as some criteria for classifying dermoscopy images. In this paper, a new approach is presented for extraction of Lyapunov exponent and Kolmogorov–Sinai entropy in the skin lesion images. This method is based on chaotic time series analysis. Converting the region of interest of skin lesion to a time series, reconstruction of system phase space, estimation of the Lyapunov exponents and calculation of Kolmogorov–Sinai entropy are the steps of the proposed approach. The combination of the largest Lyapunov exponent and Kolmogorov–Sinai entropy is selected as a criterion for distinction between melanoma and mole categories. Experiments on a set of dermoscopy images yielded a sensitivity of 100% and a specificity of 92.5% providing superior diagnosis accuracy compared to other related similar works.
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Khodadadi, H., Sedigh, A.K., Ataei, M. et al. Nonlinear Analysis of the Contour Boundary Irregularity of Skin Lesion Using Lyapunov Exponent and K-S Entropy. J. Med. Biol. Eng. 37, 409–419 (2017). https://doi.org/10.1007/s40846-017-0235-3
- Melanoma detection
- Contour boundary irregularity
- Computer aided diagnosis
- Phase space reconstruction
- Lyapunov exponent
- Kolmogorov–Sinai entropy