Automated detection of connective tissue by tissue counter analysis and classification and regression trees.

OBJECTIVE: To evaluate the feasibility of the CART (Classification and Regression Tree) procedure for the recognition of microscopic structures in tissue counter analysis. METHODS: Digital microscopic images of H & E; stained slides of normal human skin and of primary malignant melanoma were overlayed with regularly distributed square measuring masks (elements) and grey value, texture and colour features within each mask were recorded. In the learning set, elements were interactively labeled as representing either connective tissue of the reticular dermis, other tissue components or background. Subsequently, CART models were based on these data sets. RESULTS: Implementation of the CART classification rules into the image analysis program showed that in an independent test set 94.1% of elements classified as connective tissue of the reticular dermis were correctly labeled. Automated measurements of the total amount of tissue and of the amount of connective tissue within a slide showed high reproducibility (r=0.97 and r=0.94, respectively; p<0.001). CONCLUSIONS: CART procedure in tissue counter analysis yields simple and reproducible classification rules for tissue elements.