M Wiltgen1, A Gerger, J Smolle. 1. Institute of Medical Informatics, Statistics and Documentation, University of Graz, Engelgasse 13, A-8010 Graz, Austria. marco.wiltgen@uni-graz.at
Abstract
OBJECTIVE: The aim of this study was to evaluate the applicability of tissue counter analysis to the interpretation of skin images. METHOD: 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. DISCUSSION: 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. CONCLUSION: In conclusion, tissue counter analysis may be a useful method for the interpretation of melanocytic skin tumors. Copyright 2002 Elsevier Science Ireland Ltd.
OBJECTIVE: The aim of this study was to evaluate the applicability of tissue counter analysis to the interpretation of skin images. METHOD: 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. DISCUSSION: 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. CONCLUSION: In conclusion, tissue counter analysis may be a useful method for the interpretation of melanocytic skin tumors. Copyright 2002 Elsevier Science Ireland Ltd.
Authors: Nikita V Orlov; Ashani T Weeraratna; Stephen M Hewitt; Christopher E Coletta; John D Delaney; D Mark Eckley; Lior Shamir; Ilya G Goldberg Journal: Cytometry A Date: 2012-03-29 Impact factor: 4.355