R.P. Barneva et al. (Eds.): CompIMAGE 2010, LNCS 6026, pp. 151–162, 2010. © Springer-Verlag Berlin Heidelberg 2010 Numerical Methods for the Semi-automatic Analysis of Multimodal Wound Healing Images Giuseppe Placidi, Maria Grazia Cifone, Benedetta Cinque, Danilo Franchi, Maurizio Giuliani, Cristina La Torre, Guido Macchiarelli, Marta Maione, Alfredo Maurizi, Gianfranca Miconi, and Antonello Sotgiu Department of Health Sciences, University of L’Aquila, Via Vetoio Coppito 2, 67100 L’Aquila, Italy Giuseppe.Placidi@cc.univaq.it Abstract. Wound healing problem requires the analysis of tens of images from different microscopic systems. We describe a set of semi-automatic algorithms to analyze a variety of microscopy images used to study the wound healing process. The proposed suite, beside the phase contrast images, allows analyzing fluorescent microscopy images, inverted light microscopy images at different magnification and staining methods, or images obtained by scanning electron microscopy. The proposed software is designed in Matlab®. It is suggested to integrate it into the CellProfiler TM software, thus introducing new functional- ities without losing the CellProfiler existing capabilities. The approach is effi- cient, easy-to-use, and enables biologists to comprehensively and quantitatively address many questions of the wound healing problem. Keywords: wound healing, image analysis, CellProfiler, image processing. 1 Introduction Biologists are continuously involved in the visual analysis of a sample. While noth- ing can fully replace the expertise of a trained human expert, observing many sam- ples by eye is time-consuming, subjective, and non quantitative. Certain repetitive tasks in visual analysis are suitable for automation by collecting digital images and processing them with image analysis software. This has several advantages over visual observations including speed, quantitative and reproducible results, and simul- taneous measurement of many features in the image. Efforts to automate visual analysis in biology have been made, but many aspects still need improvement [7]. While numerous commercial and free software packages exist for image analysis, many of these packages are designed for a very specific purpose, such as cell count- ing [8]. Other packages are sold with accompanying hardware for image acquisition, but these are expensive and do not allow measurement of features beyond those that are already built-in. Most commercial software is proprietary, meaning that the un- derlying methods of analysis are hidden from the researcher. At the other end, some software packages are very flexible, especially for interactive analysis of individual