Image analysis of Saccharomyces cerevisiae cells: development of a plugin for IMAGEJ

• Autor(a) principal: Silva, Vitor Damião Baixinho da
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/37076
• topic: S. cerevisiae; ImageJ; Image analysis

The yeast Saccharomyces cerevisiae is one of the microorganisms with increased use at industrial, academic and scientific level. The easy growth in any culture medium, as well as the complete sequencing of its genome, are two of the main interesting factors, making this microorganism one of the most important worldwide. The morphological analysis of yeast using optical microscopy is a research area of great interest. Over the last decades, significant advances have been made in digital image processing as well in the analysis of microscopic images of cells. In this context, the development of specific software such as ImageJ is of particular interest since it is free access and open source. The main goal of this work was to develop a computer program, in the form of an extension module (plugin), in order to add certain features to ImageJ software. For this purpose, programming and processing methods were applied for a more reliable evaluation of cell and finally implemented as an ImageJ plugin. The plugin code was carried out using the Java programming language, since certain required functions were not present in the main program source code. Results presented as .xls file included the identification of cells, as well as counting and cataloguing after images processing and analysis. The features developed in this work allowed the user to process and analyse different microscopic images of S. cerevisiae cells. Finally, the plugin code was tested using multiple images of S. cerevisiae. The final version has shown high efficiency in S. cerevisiae culture images with different exposure times. Nevertheless, the plugin code was able to detect almost all cells in the images and classify them as large, normal, small and bud.