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Characterization of digital medical images utilizing support vector machines.

Maglogiannis IG, Zafiropoulos EP - BMC Med Inform Decis Mak (2004)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of the Aegean, Dept of Information and Communication Systems Engineering, Karlovasi, Greece. imaglo@aegean.gr

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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Related in: MedlinePlus

A non-linear separating region transformed in to a linear one
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Figure 4: A non-linear separating region transformed in to a linear one

Mentions: The dot product Φ(Xi)Φ(Xj) in that high dimensional space defines a kernel function k(Xi, Xj) and therefore it is not necessary to be explicit about the transformation Φ() as long as it is known that the kernel function corresponds to a dot product in some high dimensional feature space [22]. This case is presented in Figure 4.


Characterization of digital medical images utilizing support vector machines.

Maglogiannis IG, Zafiropoulos EP - BMC Med Inform Decis Mak (2004)

A non-linear separating region transformed in to a linear one
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC394338&req=5

Figure 4: A non-linear separating region transformed in to a linear one
Mentions: The dot product Φ(Xi)Φ(Xj) in that high dimensional space defines a kernel function k(Xi, Xj) and therefore it is not necessary to be explicit about the transformation Φ() as long as it is known that the kernel function corresponds to a dot product in some high dimensional feature space [22]. This case is presented in Figure 4.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: University of the Aegean, Dept of Information and Communication Systems Engineering, Karlovasi, Greece. imaglo@aegean.gr

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.

Show MeSH
Related in: MedlinePlus