The thesis is aimed on the digital analysis and processing of micro- scopic image data with a focus on cross-section samples from the artworks which fall into cultural heritage domain. It contributes to solution of two different problems of image processing - image seg- mentation and image retrieval. The performance evaluation of differ- ent image segmentation methods on a data set of cross-section images is carried out in order to study the behavior of individual approaches and to propose guidelines how to choose suitable method for segmen- tation of microscopic images. Moreover, the benefit of segmenta- tion combination approach is studied and several distinct combination schemes are proposed. The evaluation is backed up by a large number of experiments where image segmentation algorithms are assessed by several segmentation quality measures. Applicability of achieved re- sults is shown on image data of different origin. In the second part, content-based image retrieval of cross-section samples is addressed and functional solution is presented. Its implementation is included in Nephele system, an expert system for processing and archiving the material research reports with image processing features, designed and implemented for the cultural heritage application area.