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Bispectral fluorescence imaging combined with texture analysis and linear discrimination for correlation with histopathologic extent of basal cell carcinoma

Journal article
Authors Marica B Ericson
Bo Stenquist
Arne Rosén
Olle Larkö
J. Uhre
C. Strandeberg
Ann-Marie Wennberg
Published in Journal of Biomedical Optics
Volume 10
Issue 3
Pages 034009
ISSN 1083-3668
Publication year 2005
Published at Institute of Selected Clinical Sciences, Department of Dermatology and Venereology
Department of Physics (GU)
Pages 034009
Language en
Keywords Adult, Aged, Aged, 80 and over, *Algorithms, *Artificial Intelligence, Carcinoma, Basal Cell/classification/*pathology, Computer Simulation, Data Interpretation, Statistical, Discriminant Analysis, Female, Humans, Image Interpretation, Computer-Assisted/*methods, Linear Models, Male, Microscopy, Fluorescence/*methods, Middle Aged, Models, Biological, Neoplasm Invasiveness, Neoplasm Staging/methods, Reproducibility of Results, Sensitivity and Specificity, Severity of Illness Index, Skin Neoplasms/classification/*pathology, Spectrometry, Fluorescence/methods, Statistics
Subject categories Dermatology and Venereal Diseases


Fluorescence imaging has been shown to be a potential complement to visual inspection for demarcation of basal cell carcinoma (BCC), which is the most common type of skin cancer. Earlier studies have shown promising results when combining autofluorescence with protoporphyrin IX (Pp IX) fluorescence, induced by application of delta-5-aminolaevulinic acid (ALA). In this work, we have tried to further improve the ability of this technique to discriminate between areas of tumor and normal skin by implementing texture analysis and Fisher linear discrimination (FLD) on bispectral fluorescence data of BCCs located on the face. Classification maps of the lesions have been obtained from histopathologic mapping of the excised tumors. The contrast feature obtained from co-occurrence matrices was found to provide useful information, particularly for the ALA-induced Pp IX fluorescence data. Moreover, the neighborhood average features of both autofluorescence and Pp IX fluorescence were preferentially included in the analysis. The algorithm was trained by using a training set of images with good agreement with histopathology, which improved the discriminability of the validation set. In addition, cross validation of the training set showed good discriminability. Our results imply that FLD and texture analysis are preferential for correlation between bispectral fluorescence images and the histopathologic extension of the tumors.

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