Automated melanoma recognition

Harald Ganster; Ernst Wildling; Michael Binder; Harald Kittler; Axel Pinz; R. Röhrer

doi:10.1109/42.918473

Automated melanoma recognition

Harald Ganster, Ernst Wildling, Michael Binder, Harald Kittler, Axel Pinz, R. Röhrer

Publikation: Beitrag in einer Fachzeitschrift › Artikel › Begutachtung

Abstract

A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%

Originalsprache	englisch
Seiten (von - bis)	233-239
Fachzeitschrift	IEEE Transactions on Medical Imaging
Jahrgang	20
Ausgabenummer	3
DOIs	https://doi.org/10.1109/42.918473
Publikationsstatus	Veröffentlicht - 2001

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10.1109/42.918473

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title = "Automated melanoma recognition",

abstract = "A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%",

author = "Harald Ganster and Ernst Wildling and Michael Binder and Harald Kittler and Axel Pinz and R. R{\"o}hrer",

year = "2001",

doi = "10.1109/42.918473",

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journal = "IEEE Transactions on Medical Imaging",

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AU - Ganster, Harald

AU - Wildling, Ernst

AU - Binder, Michael

AU - Kittler, Harald

AU - Pinz, Axel

AU - Röhrer, R.

PY - 2001

Y1 - 2001

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AB - A system for the computerized analysis of images obtained from epiluminescence microscopy (ELM) has been developed to enhance the early recognition of malignant melanoma. As an initial step, the binary mask of the skin lesion is determined by several basic segmentation algorithms together with a fusion strategy. A set of features containing shape and radiometric features as well as local and global parameters is calculated to describe the malignancy of a lesion. Significant features are then selected from this set by application of statistical feature subset selection methods. The final kNN classification delivers a sensitivity of 87% with a specificity of 92%

U2 - 10.1109/42.918473

DO - 10.1109/42.918473

M3 - Article

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JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

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Automated melanoma recognition

Abstract

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