Bispectral fluorescence imaging combined with texture analysis and linear discrimination for correlation with histopathologic extent of basal cell carcinoma.
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.
Ericson
Marica B
MB
Chalmers University of Technology-Göteborg University, School of Physics and Engineering Physics, SE-412 96 Göteborg, Sweden. marica.ericson@fy.chalmers.se
Uhre
Jesper
J
Strandeberg
Charlotta
C
Stenquist
Bo
B
Larkö
Olle
O
Wennberg
Ann-Marie
AM
Rosén
Arne
A
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001185:Artificial Intelligence; D002280:Basal Cell Carcinoma / Q000145:classification / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008297:Male; D008856:Microscopy, Fluorescence / Q000379:methods*; D008875:Middle Aged; D008954:Models, Biological; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012878:Skin Neoplasms / Q000145:classification / Q000473:pathology*; D013050:Spectrometry, Fluorescence / Q000379:methods; D013223:Statistics as Topic