Bispectral fluorescence imaging combined with texture analysis and linear discrimination for correlation with histopathologic extent of basal cell carcinoma
Journal article, 2005
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.
Models
Aged
Computer-Assisted/*methods
Severity of Illness Index
Basal Cell/classification/*pathology
Statistics
Humans
Adult
80 and over
Discriminant Analysis
Neoplasm Invasiveness
Linear Models
Image Interpretation
Data Interpretation
Middle Aged
Carcinoma
Fluorescence/methods
Statistical
Reproducibility of Results
Fluorescence/*methods
Aged
Spectrometry
Microscopy
*Algorithms
Sensitivity and Specificity
Male
Skin Neoplasms/classification/*pathology
Female
Biological
Neoplasm Staging/methods
*Artificial Intelligence
Computer Simulation
Author
Marica B Ericson
Chalmers, Applied Physics
University of Gothenburg
Bo Stenquist
University of Gothenburg
Arne Rosen
University of Gothenburg
Olle Larkö
University of Gothenburg
J. Uhre
Sahlgrenska Universitetssjukhuset
C. Strandeberg
Sahlgrenska Universitetssjukhuset
Arne Rosen
University of Gothenburg
Journal of Biomedical Optics
1083-3668 (ISSN)
Vol. 10 3 034009-Subject Categories (SSIF 2011)
Dermatology and Venereal Diseases
DOI
10.1117/1.1925650