부산대학교 도서관

Histopathological staining is a technique widely used to highlight biological states or pharmacological activities in uman tissue. A quantitative analysis of the resulting images can produce biomarkers for diseases or even other specific conditions, thus providing valuable information for diagnosis and prognosis. Since biomarkers require measurements to be made in an objective and consistent way, software systems are employed to provide this quantitative analysis. For measurements to be reproducible, the same methods must be available across different laboratories. In this paper, we present a tool that allows users to perform quantitative analyses over the web, thus providing an efficient environment not only for individual cases to be evaluated, but also for users to share a common ground when making measurements. The classification method used by the tool to segment stained pixels is performed by a similarity function based on the polynomial version of the Mahalanobis distance, which is nonlinear and provides very robust classification for m-dimensional feature spaces. Furthermore, the similarity function can be generalized in the tool, so that images can be classified by reusing parameters of previous cases. The results of our web-based approach were compared with established ground-truth data sets, producing sensitivity, specificity and fitness values of 97.09%, 98.70%, and 97.90%.