Tissue counter analysis of benign common nevi and malignant melanoma.
OBJECTIVE
The aim of this study was to evaluate the applicability of tissue counter analysis to the interpretation of skin images.


METHODS
Digital images from microscopic views of benign common nevi and malignant melanoma were classified by the use of features extracted from histogram and co-occurrence matrix. Eighty cases were sampled and split into a training set and a test set. The images were dissected in square elements and the different features were calculated for each element. The classification was done by classification and regression trees (CART) analysis. In the CART procedure, the square elements were split into disjunctive nodes, which were characterized by a relevant subset of the features. The classification results were indicated in the original image in order to evaluate the performance of the procedure.


RESULTS
For the learning set and the test set there is a significant difference between benign nevi and malignant melanoma without overlap. Discriminant analysis based on the percentage of 'malignant elements' facilitated a correct classification of all cases.


CONCLUSIONS
Since no image segmentation was needed, problems related to this task were avoided. Though wrong classification of individual elements is unavoidable to some degree, tissue counter analysis shows a good discrimination between benign common nevi and malignant melanoma.


CONCLUSIONS
In conclusion, tissue counter analysis may be a useful method for the interpretation of melanocytic skin tumors.
Wiltgen
M
M
Institute of Medical Informatics, Statistics and Documentation, University of Graz, Engelgasse 13, A-8010 Graz, Austria. marco.wiltgen@uni-graz.at
Gerger
A
A
Smolle
J
J
D003937:Diagnosis, Differential; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008545:Melanoma / Q000175:diagnosis / Q000473:pathology*; D009508:Nevus, Pigmented / Q000175:diagnosis / Q000473:pathology*; D010781:Photography; D012044:Regression Analysis; D012878:Skin Neoplasms / Q000175:diagnosis / Q000473:pathology*