http://iet.metastore.ingenta.com
1887

Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The ABCD (asymmetry, border irregularity, colour and dermoscopic structure) rule of dermoscopy is a scoring method used by dermatologists to quantify dermoscopy findings and effectively separate melanoma from benign lesions. Automatic detection of the ABCD features and separation of benign lesions from melanoma could enable earlier detection of melanoma. In this study, automatic ABCD scoring of dermoscopy lesions is implemented. Pre-processing enables automatic detection of hair using Gabor filters and lesion boundaries using geodesic active contours. Algorithms are implemented to extract the characteristics of ABCD attributes. Methods used here combine existing methods with novel methods to detect colour asymmetry and dermoscopic structures. To classify lesions as melanoma or benign nevus, the total dermoscopy score is calculated. The experimental results, using 200 dermoscopic images, where 80 are malignant melanomas and 120 benign lesions, show that the algorithm achieves 91.25% sensitivity of 91.25 and 95.83% specificity. This is comparable to the 92.8% sensitivity and 90.3% specificity reported for human implementation of the ABCD rule. The experimental results show that the extracted features can be used to build a promising classifier for melanoma detection.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
      • 4. Stolz, W., Riemann, A., Cognetta, A., et al: ‘ABCD rule of dermatoscopy-a new practical method for early recognition of malignant-melanoma’, Eur. J. Dermatol., 1994, 4, (7), pp. 521527.
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • 9. Menzies, S.W., Crotty, K.A., Ingvar, C., et al: ‘An atlas of surface microscopy of pigmented skin lesions: dermoscopy’ (McGraw-Hill Roseville, 2003).
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • 13. Ramezani, M., Karimian, A., Moallem, P.: ‘Automatic detection of malignant melanoma using macroscopic images’, J. Med. Signals Sens., 2014, 4, (4), p. 281.
    14. 14)
    15. 15)
      • 15. Di Leo, G., Paolillo, A., Sommella, P., et al: ‘Automatic diagnosis of melanoma: a software system based on the 7-point check-list’. 2010 43rd Hawaii Int. Conf. on System Sciences (HICSS), 2010.
    16. 16)
    17. 17)
    18. 18)
      • 18. Piccolo, D., Crisman, G., Schoinas, S., et al: ‘Computer-automated ABCD versus dermatologists with different degrees of experience in dermoscopy’, Eur. J. Dermatol., 2014, 24, (4), pp. 477481.
    19. 19)
      • 19. Smaoui, N., Bessassi, S.: ‘A developed system for melanoma diagnosis’, Int. J. Comput. Vis. Signal Process., 2013, 3, (1), pp. 1017.
    20. 20)
      • 20. Ramteke, N.S., Jain, S.V.: ‘ABCD rule based automatic computer-aided skin cancer detection using Matlab®’, Int. J. Comput. Technol. Appl., 2013, 4, (4), p. 691.
    21. 21)
      • 21. Jaworek-Korjakowska, J.: Automatic detection of melanomas: an application based on the Abcd criteria', Inf. Technol. Biomed., (Springer-Berlin, Heidelberg, 2012), pp. 67–76.
    22. 22)
    23. 23)
    24. 24)
    25. 25)
      • 25. Liu, Z.Q., Cai, J.H., Buse, R.: ‘Handwriting recognition: soft computing and probabilistic approaches’ (Springer, 2012).
    26. 26)
    27. 27)
    28. 28)
    29. 29)
      • 29. Mokrani, K., Kasmi, R., Arour, M.: ‘Technique d’élimination des poils pour les images dermoscopiques’. National Conf. on Electronics and New Technologies, 2015.
    30. 30)
      • 30. Wang, H., Chen, X., Moss, R.H., et al: ‘Watershed segmentation of dermoscopy images using a watershed technique’, Skin Res. Technol., 2010, 16, (3), pp. 378384.
    31. 31)
    32. 32)
    33. 33)
      • 33. Lee, C.P.: ‘Robust image segmentation using active contours: level set approaches’. PhD thesis, North Carolina State University, 2005.
    34. 34)
    35. 35)
    36. 36)
      • 36. Sethian, J.A.: ‘Level set methods and fast marching methods’, J. Comput. Inf. Technol., 2003, 11, (1), pp. 12.
    37. 37)
      • 37. Kasmi, R., et al: ‘Biologically inspired skin lesion segmentation using a geodesic active contour technique,Skin Res. Technol., 2015, doi: 10.1111/srt.12252.
    38. 38)
    39. 39)
    40. 40)
      • 40. Risson, V.: ‘Application De La Morphologie Mathématique À L'analyse Des Conditions D'éclairage Des Images Couleur’ (École Nationale Supérieure des Mines de Paris, 2001).
    41. 41)
      • 41. Farrugia, J.P.: ‘Modèles De Vision Et Synthèse D'images’ (Ecole Nationale Supérieure des Mines de Saint-Etienne; Université Jean Monnet-Saint-Etienne, 2002).
    42. 42)
      • 42. Sharma, G., Bala, R.: ‘Digital colour imaging handbook’ (CRC Press, 2002).
    43. 43)
      • 43. Seidenari, S., Pellacani, G., Grana, C.: ‘Early detection of melanoma by image analysis’, Bioeng. Skin, Skin Imaging Anal., 2006, 31, pp. 305311.
    44. 44)
      • 44. Grammatikopoulos, G., Hatzigaidas, A., Papastergiou, A., et al: ‘Automated malignant melanoma detection using Matlab’. Proc. Fifth Int. Conf. on Data Networks, Communications and Computers, Bucharest, Romania, 2006.
    45. 45)
    46. 46)
      • 46. Ng, V.T., Lee, T.K.: ‘Measuring border irregularities of skin lesions using fractal dimensions’. Photonics China ‘96, Int. Society for Optics and Photonics, 1996.
    47. 47)
    48. 48)
    49. 49)
      • 49. Argenziano, G., Soyer, H.P., De Giorgio, V., et al: ‘Interactive atlas of dermoscopy (book and CD-ROM)’ (EDRA Medical Publishing and New Media, 2000).
    50. 50)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2015.0385
Loading

Related content

content/journals/10.1049/iet-ipr.2015.0385
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address