By Jan P. A. Baak M.D., Ph.D., Jan Oort M.D., Ph.D. (auth.)
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Extra info for A Manual of Morphometry in Diagnostic Pathology
5. Combination of features may decrease the overlap. H ~------~~------~------Xl I 1I Then the classification of new patients is reduced to the question of whether the y-value of the patient in the decision rule formulated in y-values is right or left from the decision threshold T? This illustrates how multivariate combinations can be reduced, sometimes even to one dimensional linear decisions. This type of analysis is called linear discriminant analysis. 9). 7 Number of Features and Objects Used in the Analysis Three considerations are essential at this point.
115, 137-150. J. (1980). Fundamental tissue geometry for the biologist. Cambridge University Press, Cambridge. R. (1979). Stereological Methods. Vol. I. Practical methods for biological morphometry. 257-348. A. Baak and J. Oort In chapters 6 and 7 applications of morphometry in diagnostic human histo- and cytopathology are described. Before the quantitative techniques and data to be described are actually used and applied in diagnostic pathology, a word of warning is necessary. First; the results of quantitative analysis of a particular microscopical section should be interpreted by the pathologist.
Firstly, such good discrimination as shown in fig. 5 will be obtained only rarely with two features. With more than two features the number of correctly classified cases may increase (see fig. 6). Secondly, even with more than two features in the multivariate analysis, 100% correct classifications will not always be obtained, as is also illustrated in fig. 6. Since the graph levels off after five features, inclusion of more than five features 33 100 90 80 PERCENTAGE CORRECTL Y CLASSIFIED 70 60 2 3 4 5 6 7 NUMBER OF FEATURES IN THE ANALYSIS Fig.
A Manual of Morphometry in Diagnostic Pathology by Jan P. A. Baak M.D., Ph.D., Jan Oort M.D., Ph.D. (auth.)