0 Automated Segmentation and Morphometry of Cell and Tissue Structures. Selected Algorithms in ImageJ Dimiter Prodanov and Kris Verstreken Bioelectronic systems group, BIONE, Imec Belgium 1. Introduction This chapter covers selected aspects of the segmentation and measurements of spatial or temporal features (i.e. morphometry) of biological objects in biomedical (non-optical) 1 and microscopic images. The term measurement refers to a succinct quantitative representation of image features over space and time. This implies the application of the act of geometric measurement to the raw imaging data, i.e. "morphometry". Measurements arise in a defined experimental context. 1.1 Information complexity aspects The life science experimentation strives to answer defined research questions via quantitative analysis of multiple experimental trials. This process can be described by a workflow 2 which starts by defining the research hypotheses or questions (Fig. 1). During the last stage the images are transformed into measurements, which are finally interpreted in the light of the original research question (Fig. 1). A substantial decrease of the volume of output data occurs at each step of the so-described processing workflow. In contrast, this decrease is translated into an increase of the complexity of generated information (e.g. derived data). For example, if one takes a microscopic image representing a cell and measures its shape, then the raster image data (supposedly a matrix of width x height) transforms into a set of shape parameters, each one having a different semantic context (for example, neurite length, orientation, cell size). While in the raster data set the biological object is only implicitly present, in the derived data the representation of at least one attribute of the object under study is explicitly constructed (for example, the cell size). At this stage, the explicit information contained in the raw image about the illumination and staining distribution is lost. Therefore, the process of object (i.e. pattern) recognition and reconstruction is accompanied by irreversible reduction of the input information. At each step of the workflow the information in the previous step is transformed into contextual data, 1 The bioluminescence imaging methods will not be discussed here. 2 A workflow provides an abstracted view over the experiment being performed. It describes what procedures need to be enacted, but not necessarily all the details of how they will be executed. 8