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Cervical cancer can be cured if detected and treated early, for which the Pap test has been fundamental. In this context, technological aids can reduce the subjective nature of the diagnosis, although there are still several issues to be solved. Here we address two of them: the identification of the cytoplasm and the nucleus in a cell image, and the determination of a set of relevant features for the detection of injured cells. In this paper we present two contributions. First, we propose an interactive segmentation method based on multispectral morphological processing in which most misleading imperfections are eliminated with a simple interaction of the analyst. Second, we made an analysis of the relevance of certain variables that characterize the relative sizes of nucleus and cytoplasm, their shapes, textures and roughness of the edges. The analysis is based on performance measures of a detector that uses feature extraction through principal component analysis (PCA) and separation of normal and injured cells by a support vector machine (SVM). We have found that minimal interaction with the physician allows for much more accurate and reliable segmentation than purely automatic methods. On the other hand, we have found that the most important characteristics for detection of injured cells are the relative sizes of the nucleus and the cytoplasm and their form, while other features, such as texture and roughness, are less relevant. [ABSTRACT FROM AUTHOR]