Tissue counter analysis of histologic sections of melanoma: Influence of mask size and shape, feature selection, statistical methods and tissue preparation

Article type: Research Article

Authors: Smolle, Josef^; | Gerger, Armin | Weger, Wolfgang | Kutzner, Heinz | Tronnier, Michael

Affiliations: Department of Dermatology, Division of Analytical Morphologic Dermatology, Graz, Austria | Dermatohistopathologische Gemeinschaftspraxis, Friedrichshafen, Germany | Department of Dermatology and Venereology, Luebeck, Germany

Note: [] Corresponding author: J. Smolle, Department of Dermatology, Division of Analytical‐Morphologic Dermatology, University of Graz, Auenbruggerplatz 8, A‐8036 Graz, Austria. Tel.: +43 316 385/2530; Fax: +43 316 385/2466; E‐mail: josef.smolle@kfunigraz.ac.at.

Abstract: Background: Tissue counter analysis is an image analysis tool designed for the detection of structures in complex images at the macroscopic or microscopic scale. As a basic principle, small square or circular measuring masks are randomly placed across the image and image analysis parameters are obtained for each mask. Based on learning sets, statistical classification procedures are generated which facilitate an automated classification of new data sets. Objective: To evaluate the influence of the size and shape of the measuring masks as well as the importance of feature selection, statistical procedures and technical preparation of slides on the performance of tissue counter analysis in microscopic images. As main quality measure of the final classification procedure, the percentage of elements that were correctly classified was used. Study design: H&E‐stained slides of 25 primary cutaneous melanomas were evaluated by tissue counter analysis for the recognition of melanoma elements (section area occupied by tumour cells) in contrast to other tissue elements and background elements. Circular and square measuring masks, various subsets of image analysis features and classification and regression trees compared with linear discriminant analysis as statistical alternatives were used. The percentage of elements that were correctly classified by the various classification procedures was assessed. In order to evaluate the applicability to slides obtained from different laboratories, the best procedure was automatically applied in a test set of another 50 cases of primary melanoma derived from the same laboratory as the learning set and two test sets of 20 cases each derived from two different laboratories, and the measurements of melanoma area in these cases were compared with conventional assessment of vertical tumour thickness. Results: Square measuring masks were slightly superior to circular masks, and larger masks (64 or 128 pixels in diameter) were superior to smaller masks (8 to 32 pixels in diameter). As far as the subsets of image analysis features were concerned, colour features were superior to densitometric and Haralick texture features. Statistical moments of the grey level distribution were of least significance. CART (classification and regression tree) analysis turned out to be superior to linear discriminant analysis. In the best setting, 95% of melanoma tissue elements were correctly recognized. Automated measurement of melanoma area in the independent test sets yielded a correlation of r=0.846 with vertical tumour thickness (p<0.001), similar to the relationship reported for manual measurements. The test sets obtained from different laboratories yielded comparable results. Conclusions: Large, square measuring masks, colour features and CART analysis provide a useful setting for the automated measurement of melanoma tissue in tissue counter analysis, which can also be used for slides derived from different laboratories.

Keywords: Melanoma, image analysis, tissue counter analysis, classification and regression tree, texture analysis, colour analysis

Journal: Analytical Cellular Pathology, vol. 24, no. 2-3, pp. 59-67, 2002