ANALYSES OF SKIN LESION AREAS AFTER THRESHOLDING

Magdalena Michalska

mmagamichalska@gmail.com
Lublin University of Technology (Poland)

Abstract

Melanoma is one of the fastest spreading cancers. The aim of the article is to segment the skin lesions from human skin dermatoscopic images covered by melanoma. Threshold segmentation was used, which allows a single skin lesion to be analyzed. It shows the four areas of each based on their color. The created software monitors the border of skin lesion areas. Segmentation and analysis of the resulting images with different areas of skin change was carried out in the Matlab software.


Keywords:

dermatoscopy, melanoma, thresholding, image region analize, dermatoscopy, melanoma, thresholding, image region analysis

Argenziano G., Catricalà C., Ardigo M.: Seven-point checklist of dermoscopy revisited. The British Journal of Dermatology 4, 2011, 785–90.
DOI: https://doi.org/10.1111/j.1365-2133.2010.10194.x   Google Scholar

Breslow A.: Thickness, cross-sectional areas and depth of invasion in the prognosis of cutaneous melanoma. Annals of Surgery 172, 1970, 902–908.
DOI: https://doi.org/10.1097/00000658-197011000-00017   Google Scholar

Celebi M. E., Kingravi H. A., Uddin B.: A methodological approach to the classification of dermoscopy images. Computerized Medical Imaging and Graphics 2007, 362–373.
DOI: https://doi.org/10.1016/j.compmedimag.2007.01.003   Google Scholar

Celebi M. E., Wen Q., Hwang S., Iyatomi H., Schaefer G.: Lesion border detection in dermoscopy images using ensembles of thresholding methods. Skin Res. Technol. 19 (1), 2013, 252–258.
DOI: https://doi.org/10.1111/j.1600-0846.2012.00636.x   Google Scholar

Clark W. H., From L., Bernardino E. A.: Histogenesis and biologic behavior of primary human malignant melanomas of the skin. Cancer Research 29, 1969, 705–726.
  Google Scholar

Damilola A., Okuboyejo O.: Automating skin disease diagnosis using image classifications. Proceedings of the world congress on engineering and computer science II, San Francisco 2013.
  Google Scholar

Dermatoscopy images database: https://www.dermis.net/dermisroot/en/list/m/search.htm (accessed: 20.03.2020).
  Google Scholar

Dermatoscopy images database: https://www.isic-archive.com/ (accessed: 20.03.2020).
  Google Scholar

Emery J. D, Hunter J., Hall P. N.: Accuracy of siascopy for pigmented skin lesions encountered in primary care: development and validation of a new diagnostic algorithm. BMC Dermatology 10, 2010, 1–9.
DOI: https://doi.org/10.1186/1471-5945-10-9   Google Scholar

Fiorese, M., Peserico, E., Silletti, A.: VirtualShave: automated hair removal from digital dermatoscopic image. Proc. IEEE EMBS, 2011, 5145–5148.
DOI: https://doi.org/10.1109/IEMBS.2011.6091274   Google Scholar

Ganster H., Pinz A., R¨ohrer R.: Automated melanoma recognition medical imaging. IEEE Transactions 20(3), 2001, 233–239.
DOI: https://doi.org/10.1109/42.918473   Google Scholar

Henning J., Dusza S., Wang S.: The cash (color, architecture, symmetry, and homogeneity) algorithm for dermoscopy. Archives of Dermatology 56, 2007, 45–52.
DOI: https://doi.org/10.1016/j.jaad.2006.09.003   Google Scholar

https://www.mathworks.com/help/images/pixel-values-and-image-statistics.html (accessed: 20.03.2020).
  Google Scholar

Huang, A., Kwan, S., Chang, W., Liu, M., Chi, M., Chen, G.: A robust hair segmentation and removal approach for clinical images of skin lesions. Proc. IEEE EMBS 2013, 3315–3318.
DOI: https://doi.org/10.1109/EMBC.2013.6610250   Google Scholar

Jahanifar M., Tajeddin N. Z., Mohammadzadeh Asl B., Gooya A.: Supervised saliency map driven segmentation of lesions in dermoscopic images. IEEE Journal of Biomedical and Health Informatics 23(2), 2019, 509–518.
DOI: https://doi.org/10.1109/JBHI.2018.2839647   Google Scholar

Kiani, K., Sharafat, A.R.: E-shaver: An improved dullrazor for digitally removing dark and light-colored hairs in dermoscopic images. Comput. Biol. Med. 41(3), 2011, 139–145.
DOI: https://doi.org/10.1016/j.compbiomed.2011.01.003   Google Scholar

Kittler H., Riedl E., Rosendahl C.: Dermatoscopy of unpigmented lesions of the skin: a new classification of vessel morphology based on pattern analysis. Dermapathology. Practical and Conceptual 14, 2008, 3–7.
  Google Scholar

Koehoorn J., Sobiecki A. C., Boda D., Diaconeasa A., Doshi S., Paisey S., Jalba A., Telea A.: Automated digital hair removal by threshold decomposition and morphological analysis. International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing 9082, 2015, 15–26.
DOI: https://doi.org/10.1007/978-3-319-18720-4_2   Google Scholar

Korjakowska J. J.: Automatic detection of melanomas: An application based on the abcd criteria. Springer 7339, 2012, 67–76.
DOI: https://doi.org/10.1007/978-3-642-31196-3_7   Google Scholar

Korotkov K., Garcia R.: Computerized analysis of pigmented skin lesions: A review. Artificial Intelligence in Medicine 56(2), 2012, 69–90.
DOI: https://doi.org/10.1016/j.artmed.2012.08.002   Google Scholar

Leo G. D., Paolillo A., Sommella P., G. Fabbrocini G., Rescigno O.: A software tool for the diagnosis of melanomas. IEEE Instrumentation and Measurement Technology Conference 2010, 886–891.
  Google Scholar

Maglogiannis I., Pavlopoulos S., Koutsouris D.: An integrated computer supported acquisition, handling, and characterization system for pigmented skin lesions in dermatological images. IEEE Transactions on Information Technology in Biomedicine 2005, 86–98.
DOI: https://doi.org/10.1109/TITB.2004.837859   Google Scholar

Mendonca T., Ferreira P. M., Marques J. S., Marcal A. R., Rozeira J.: A dermoscopic image database for research and benchmarking. 35th Annual International Conference of the IEEE EMBS Osaka 2013, 5437–5440.
DOI: https://doi.org/10.1109/EMBC.2013.6610779   Google Scholar

Michalska M.: Przegląd sposobów segmentacji zmian skórnych. Interdyscyplinarne prace doktorantów Politechniki Lubelskiej 2019, 33-45.
  Google Scholar

Michalska M.: Wykorzystanie segmentacji przez progowanie w wykrywaniu czerniaka skóry. Wybrane zagadnienia z zakresu elektrotechniki, inżynierii biomedycznej i budownictwa prace doktorantów Politechniki Lubelskiej 2019, 147–157.
  Google Scholar

Michalska M., Hotra O.: Quality analysis of dermatoscopic images thresholding with malignant melanoma, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 768–774
DOI: https://doi.org/10.1117/12.2536671   Google Scholar

Oliveira R. B., Filho E. M., Ma Z., Papa J. P., Pereira A. S., Tavares J. M. R. S.: Computational methods for the image segmentation of pigmented skin lesions: A review. Comput. Methods Programs Biomed. 131, 2016, 127–141.
DOI: https://doi.org/10.1016/j.cmpb.2016.03.032   Google Scholar

Przystalski K.: Detekcja i klasyfikacja barwnikowych zmian skóry na zdjęciach wielowarstwowych [PhD thesis]. Warszawa 2014.
  Google Scholar

Rosendahl C., Cameron A., McColl I., Wilkinson D.: Dermatoscopy in routine practice Chaos and Clues. Australian Family Physician 41(7), 2012, 482–487.
  Google Scholar

Soyer P., Argenziano G., Zalaudek I.: Three-point checklist of dermoscopy. Dermatology 208, 2004, 27–31.
DOI: https://doi.org/10.1159/000075042   Google Scholar

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Published
2020-09-30

Cited by

Michalska, M. (2020). ANALYSES OF SKIN LESION AREAS AFTER THRESHOLDING. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 10(3), 9–12. https://doi.org/10.35784/iapgos.1603

Authors

Magdalena Michalska 
mmagamichalska@gmail.com
Lublin University of Technology Poland

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