A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions

The aim of the study was to provide mathematical descriptors for the border of pigmented skin lesion images and to assess their efficacy for distinction among different lesion groups. New descriptors such as lesion slope and lesion slope regularity are introduced and mathematically defined. A new al...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2003-08, Vol.22 (8), p.959-964
Main Authors: Grana, C., Pellacani, G., Cucchiara, R., Seidenari, S.
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Data mining
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted
Interpolation
Lesions
Malignant tumors
Medical sciences
Melanoma - classification
Melanoma - pathology
Microscopy
Microscopy, Polarization - methods
Nevus, Pigmented - classification
Nevus, Pigmented - pathology
Optical polarization
Pigmentation
Reproducibility of Results
Sensitivity and Specificity
Skin
Skin Neoplasms - classification
Skin Neoplasms - pathology
Spline
Testing
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Summary:The aim of the study was to provide mathematical descriptors for the border of pigmented skin lesion images and to assess their efficacy for distinction among different lesion groups. New descriptors such as lesion slope and lesion slope regularity are introduced and mathematically defined. A new algorithm based on the Catmull-Rom spline method and the computation of the gray-level gradient of points extracted by interpolation of normal direction on spline points was employed. The efficacy of these new descriptors was tested on a data set of 510 pigmented skin lesions, composed by 85 melanomas and 425 nevi, by employing statistical methods for discrimination between the two populations.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2003.815901