Abstract
Malignant melanoma, which is a dangerous proliferation of melanocytes, is commonly diagnosed in all people, regardless of age, gender, or race. In the last several years an increasing melanoma incidence and mortality rate has been observed worldwide and it is rising faster than other forms of cancer. In this paper we present a new approach to the detection and classification of pigment network, one of the major feature in a widely used diagnostic algorithm 7-point checklist. Accurate assessment of pigment network is clinically important due to a significantly different occurrence in benign and malignant skin lesions. We describe a complex algorithm containing following steps: image enhancement, lesion segmentation, pigment network detection as well as classification. The algorithm has been tested on 300 dermoscopic images and achieved 91% sensitivity and classification accuracy of 85%. Compared to state-of-the-art, we obtain improved classification accuracy.
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References
Argenziano, G., et al.: Interactive Atlas of Dermoscopy. Edra Medical Publishing and New Media, Milano (2000)
Cancer Research UK, Skin cancer Key Facts (2012). www.cancerresearchuk.org/
Jaworek-Korjakowska J.: Automatic detection of melanomas: an application based on the ABCD criteria. In: ITIB Proceedings in LNCS, pp. 67–76, Springer, Heidelberg (2012)
Sadeghi, M., Razmara, M., Lee, T., Stella Atkins, M.: A novel method for detection of pigment network in dermoscopic images using graphs. Comput. Med. Imag. Graph. 35(2), 137–143 (2011)
Sadeghi, M., Razmara, M., Wighton, P., Lee, T., Stella Atkins, M.: Modeling the dermoscopic structure pigment network using a clinically inspired feature set. In: MIAR. LNCS, pp. 467–474. Springer, Heidelberg (2010)
Fleming, M., Steger, C., et al.: Techniques for a structural analysis of dermatoscopic imagery. Comput. Med. Imag. Graph. 22(5), 375–389 (1998)
Jaworek-Korjakowska J., Tadeusiewicz R.: Assessment of dots and globules in dermoscopic color images as one of the 7-point checklist criteria. In: Proceedings of IEEE International Conference on Image Processing (ICIP 2013), Melbourne, Australia, pp. 1456–1460 (2013)
Jaworek-Korjakowska, J., Tadeusiewicz, R.: Hair removal from dermoscopic color images. Bio Alg. Med Syst. 9(2), 53–58 (2013)
Acknowledgments
This work was supported by AGH University of Science and Technology research grant No. 11.11.120.612.
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Jaworek-Korjakowska, J., Kłeczek, P., Tadeusiewicz, R. (2018). Detection and Classification of Pigment Network in Dermoscopic Color Images as One of the 7-Point Checklist Criteria. In: Augustyniak, P., Maniewski, R., Tadeusiewicz, R. (eds) Recent Developments and Achievements in Biocybernetics and Biomedical Engineering. PCBBE 2017. Advances in Intelligent Systems and Computing, vol 647. Springer, Cham. https://doi.org/10.1007/978-3-319-66905-2_15
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DOI: https://doi.org/10.1007/978-3-319-66905-2_15
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