Modeling Cell Migration in Quantitative Image Analysis

• Autor(a) principal: Da Silva, Patrícia Andreia Cirne
• Data de Publicação: 2011
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10451/13888
• topic: particle tracking; plugin; ImageJ; quantitative image analysis; Cell migration

All biological phenomena are dynamic and movement is an essential function in cellular systems but their regulation, characteristics and physiological meaning are not fully known. Measurement of the cell movements provides quantitative information that is inevitable for understanding the cellular system. Cell migration is a field of intense current research generating high amounts of image data that need to be quantitatively analyzed with efficiency, consistency and completeness. To accomplish, computerized motion analysis is rapidly becoming a requisite. Since all the existing algorithms for this purpose are often not robust, effective and optimal enough to yield satisfactory results new and alternative methods must be developed. The aim of this work is to find and develop an alternative to the tracking of individual cells in order to, visualize, characterize and quantify the migration characteristics of cell population. This alternative comprises the implementation of a simple and automated algorithm to obtain qualitative and quantitative information from image sequences of cell migration in a fast, easy and inexpensive computationally way. After an extensive literature review, it became clear that all the methodologies and approaches employed to make the quantitative analysis of cell migration only presented solutions that involved object tracking. And the new method developed estimates the probability density functions for cell migration and was implemented as a plugin (Migration) for ImageJ, as cross platform open source application. For the evaluation of the developed algorithm was taken in to account his applicability, efficiency, consistency, completeness and validity. It can be used to process image sequences to extract all information regarding the estimation of the future positions of all particles in a determined time point in the future and is quick when is executing. Comparing to existing approaches to study the cell migration this method adds an improvement, it can deal with complex situation, such as overlapping of particles or other occlusions.