Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions


The early diagnosis of Melanoma is a challenging task for dermatologists, because of the characteristic similarities of Melanoma with other skin lesions such as typical moles and dysplastic nevi. Aims: This work aims to help both experienced and non-experienced dermatologists in the early detection of cutaneous Melanoma through the development of a computational helping tool based on the “ABCD” rule of dermoscopy. Moreover, it aims to decrease the need for invasive biopsy procedure for each tested abnormal skin lesion. Methods: This is accomplished through the utilization of MATLAB programming language to build a feature extraction tool for the assessment of lesion asymmetry, borders irregularity, and colors variation in the tested lesion. Results: The helping tool obtained a sensitivity of 81.48%, a specificity of 52.83% and accuracy of 62.50% in the assessment of the Asymmetry Index. A new metric for the borders irregularity index was built. Finally, for the Colors Variation Index algorithm a sensitivity of 51.37%, a specificity of 61.51% and accuracy of 57.80% was achieved. Conclusions: This work created a computational tool based on the ABCD-rule, which is helpful for both experienced and non-experienced dermatologists in the early discrimination of Melanoma than other types of skin lesions and to eliminate the need of the biopsy procedure. A new metric for the Borders Irregularity Index was established depending on the number of inflection points in the lesion’s borders.


  • Argenziano G, Soyer HP, De Giorgi V, Piccolo D, Carli P, Delfino M, Ferrari A, et al. (2000) Interactive Atlas of Dermoscopy. EDRA Medical Publishing & New Media.
  • Atkins MS, Lee TK (2000) A New Approach to Measure Border Irregularity for Melanocytic Lesions. (July 2017).
  • Chaves-gonzález JM, Vega-rodríguez MA, Gómez-pulido JA, Sánchez-pérez JM (2010) Detecting Skin in Face Recognition Systems: A Colour Spaces Study. Digital Signal Processing 20(3): 806-823.
  • Day GR, Barbour RH (2000) Automated Melanoma Diagnosis: Where Are We At? Skin Research and Technology 6(1): 1-5.
  • Dong C, Liang G, Hu B, Yuan H, Jiang Y, Zhu H (2018) Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods. (June): 1-11.
  • Erdoğan K, Yılmaz N (2015) Shifting Colors to Overcome Not Realizing Objects Problem Due to Color Vision Deficiency. (December 2014): 10-14.
  • Jain S, Jagtap V, Pise N (2015) Computer Aided Melanoma Skin Cancer Detection Using Image Processing. Procedia Computer Science 48(C): 736-741.
  • Lazaridis P, Zaharis ZD, Kampitaki DG (2007) Simple Matlab Tool for Automated Malignant Melanoma Diagnosis Simple Matlab Tool for Automated Malignant Melanoma Diagnosis. (December 2013).
  • Loane M, Gore H, Corbett R, Steele K, Mathews C, Bloomer S, Eedy D, Telford R, Wootton R (1997) Effect of Camera Performance on Diagnostic Accuracy: Preliminary Results from the Northern Ireland Arms of the Uk Multicentre Teledermatology Trial. Journal of Telemedicine and Telecare 3(2): 83-88.
  • Masood A, Al-jumaily AA (2017) Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms. International Journal of Biomedical Imaging 2013: 11-12.
  • Mendonc T, Ferreira PM, Marques JS (2013) PH 2 - A Dermoscopic Image Database for Research and Benchmarking*: 5437-5440.
  • Muchun W, Wenzhong Z, Siqi Z (2018) The Relationship Between Low-Carbon Finance and Sustainable Development: A Case Study of Industrial Bank of China. International Journal of Sustainable Energy and Environmental Research, 7(1): 24-34.
  • Oliveira RB, Papa JP, Pereira AS, Tavares JMRS (2018) Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends. Neural Computing and Applications 29(3): 613-636.
  • Rastgoo M, Garcia R, Morel O, Marzani F (2015) Automatic Differentiation of Melanoma from Dysplastic Nevi. Computerized Medical Imaging and Graphics 43: 44-52.
  • Stolz W, Riemann A, Cognetta AB (1994) ABCD Rule of Dermatoscopy: A New Practical Method for Early Recognition of Malignant Melanoma. European Journal of Dermatology 4: 521-527.


This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.