Background: To identify nuclei and lesions with great specificity, a large set of karyometric features is arranged in the form of a linear profile, called a nuclear signature. The karyometric feature values are normalized as z-values. Their ordering along the profile axis is arbitrary but consistent. The profile of the nuclear signature is distinctive; it can be characterized by a new set of variables called contour features. A number of data reduction methods are introduced and their performance is compared with that of the karyometric features in the classification of prostatic, colonic, and esophageal lesions.
Methods: Contour characteristics were reduced to descriptive statistics of the set of z-values in the nuclear signature and to sequence information. The contour features derived were (1) relative frequencies of occurrence of z-values and of their differences and (2) co-occurrence statistics, run lengths of z-values, and statistics of higher-order dependencies. Performance was evaluated by comparing classification scores of diagnostic groups.
Results: Rates for correct classification by karyometric features alone and contour features alone indicate equivalent performance. Classification by a combined set of features led to an increase in correct classification.
Conclusions: Image analysis and subsequent data reduction of nuclear signatures of contour features is a novel method, providing quantitative information that may lead to an effective identification of nuclei and lesions.
Copyright 2000 Wiley-Liss, Inc.