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. 2004 Mar 10:4:4.
doi: 10.1186/1472-6947-4-4.

Characterization of digital medical images utilizing support vector machines

Ilias G Maglogiannis  1 , Elias P Zafiropoulos
Affiliations

Characterization of digital medical images utilizing support vector machines

Ilias G Maglogiannis et al. BMC Med Inform Decis Mak. .

Abstract

Background: In this paper we discuss an efficient methodology for the image analysis and characterization of digital images containing skin lesions using Support Vector Machines and present the results of a preliminary study.

Methods: The methodology is based on the support vector machines algorithm for data classification and it has been applied to the problem of the recognition of malignant melanoma versus dysplastic naevus. Border and colour based features were extracted from digital images of skin lesions acquired under reproducible conditions, using basic image processing techniques. Two alternative classification methods, the statistical discriminant analysis and the application of neural networks were also applied to the same problem and the results are compared.

Results: The SVM (Support Vector Machines) algorithm performed quite well achieving 94.1% correct classification, which is better than the performance of the other two classification methodologies. The method of discriminant analysis classified correctly 88% of cases (71% of Malignant Melanoma and 100% of Dysplastic Naevi), while the neural networks performed approximately the same.

Conclusion: The use of a computer-based system, like the one described in this paper, is intended to avoid human subjectivity and to perform specific tasks according to a number of criteria. However the presence of an expert dermatologist is considered necessary for the overall visual assessment of the skin lesion and the final diagnosis.

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Figures

Figure 1
Figure 1
A typical malignant melanoma. (b) Dysplastic naevus
Figure 2
Figure 2
Dysplastic naevus
Figure 3
Figure 3
(a) The hyperplanes HI, H2 located so that no data points lie between them. (b) The points a, b, c, d and e are the support vectors.
Figure 4
Figure 4
A non-linear separating region transformed in to a linear one
Figure 5
Figure 5
A generic feed-forward neural network
See this image and copyright information in PMC

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