Scholarly record
COLOUR HISTOGRAM ANALYSIS FOR SKIN LESIONS DISCRIMINATION
Abstract
One of the risk factors in skin cancer is unprotected exposure to UV radiation. Melanoma is the deadliest form of skin cancer but also curable if it is diagnosed in the early stages. This study uses a histogram analysis technique to investigate and classify two common skin lesions, nevi and melanoma. The colour feature investigated is the optimal threshold values in RGB space. Also, a boundary limit of the skin lesions is performed using a colour histogram analysis and the optimal threshold determination. A statistical analysis of the relevant threshold values for R, G and B histograms to establish a sound colour separation between benign and malignant lesions is performed. Monochromatic channel threshold values are clustered using K-means and the validity of clustering is assessed by silhouette method. The proposed method technique is evaluated using 120 colour digital images (60 melanomas and 60 nevi) from the Digital Archive of the Department of Dermatology of the University Medical Center in Groningen (UMCG)1. The reported results suggest red as the lesion representative colour in dermoscopic images. The pixel values for red channel are strongly related to the skin lesion type.
Publication Impact Profile
Publication details
References16
Gareau D.S., Correa da Rosa J, Yagerman S., Carucci J.A., Gulati N., Hueto F., DeFazio J.L., Suárez-Fariñas M., Marghoob A., Krueger J.G., Digital imaging biomarkers feed machine learning for melanoma screening, Experimental Dermatology, vol. 26, no. 7, pp. 615–618, 2017.
Barata C., Figueiredo M., Celebi M. E., Marques J., Color identification in dermoscopy images using Gaussian mixture models, IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP) - Proceedings,
Lingala M., Stanley R.J., Rader R.K., Hagerty J., Rabinovitz H.S., Oliviero M., Choudhry I., Stoecker W.V., Fuzzy logic color detection: Blue areas in melanoma dermoscopy images, Comput Med Imaging Graph, vol. 38, no. 5, pp. 403–410, 2014.
Damian F.A., Moldovanu S., Moraru L., Skin lesions analysis based on significant features selection algorithmn, 8th International Conference on Experiments/Process/System Modeling/Simulation/Optimization, Greece, vol. ll, pp 380-387, 2019.
Rosendahl C., Cameron A., McColl I., Wilkinson D., Dermatoscopy in routine practice - ‘chaos and clues’, Australian Family Physician, Australia, vol. 41, no. 7, pp. 482–487, 2012.
Nachbar F., Stolz W., Merkle T., Cognetta A.B., Vogt T., Landthaler M., Bilek P., Braun-Falco O., Plewig G., The ABCD rule of dermatoscopy. High prospective value in the diagnosis of doubtful melanocytic skin lesions, Journal of the American Academy of Dermatology, vol. 30, no. 4, pp. 551–559, 1994.
Ferris L.K., Harkes J.A., Gilbert B., Winger D.G., Golubets K., Akilov O., Satyanarayanan M., Computer-aided classification of melanocytic lesions using dermoscopic images, Journal of the American Academy of Dermatology, vol. 73, no. 5, pp. 769–776, 2015.
Bajaj S., Marchetti M., Navarrete-Dechent C., Dusza S., Kose K., Marghoob A., The Role of Color and Morphologic Characteristics in Dermoscopic Diagnosis, JAMA Dermatology, vol. 152, no. 6, pp. 676–682, 2016.
Barata C., M. Ruela, Francisco M., Mendonça T., Marques J., Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features, IEEE Systems Journal, vol. 8, no. 3, pp. 965–979, 2014.
Seidenari S., Pellacani G., Costantino G., Automated assessment of pigment distribution and color areas for melanoma diagnosis, in Handbook of Non-Invasive Methods and the Skin, J. Serup, B. E. Jemec, and G. L. Grove, Eds., chapter 18, pp. 135–146, Informa Healthcare, CRC/Taylor & Francis, 2nd edition, 2006.
Pereira P., Tavora L., Fonseca-Pinto R., Paiva R.P., Assunção P., De Faria S., Image Segmentation using Gradient-based Histogram Thresholding for Skin Lesion Delineation, 6th International Conference on Bioimaging, DOI: 10.5220/0007354100840091, pp. 84-91, 2019.
Otsu N., A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
Manikandan S., Frequency distribution, Journal of Pharmacology & Pharmacotherapeutics, vol.2, no. 1, pp. 54–56, 2011
Onoda T., Sakai M., Yamada S., Independent Component Analysis Based Seeding Method for K-Means Clustering, IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, vol. 3, pp. 122–125, 2011.
Dewi C., Clustering of high resolution UAV imagery to identify essential plants using SOM neural network, Journal of Environmental Engineering and Sustainable Technology, vol. 4, no. 1, pp. 55–63, 2017.
Rousseeuw P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987.
Citing literature
Number of times cited according to Crossref: 1
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.
