2 resultados para Digital image processing

em CiencIPCA - Instituto Politécnico do Cávado e do Ave, Portugal


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Protein aggregation became a widely accepted marker of many polyQ disorders, including Machado-Joseph disease (MJD), and is often used as readout for disease progression and development of therapeutic strategies. The lack of good platforms to rapidly quantify protein aggregates in a wide range of disease animal models prompted us to generate a novel image processing application that automatically identifies and quantifies the aggregates in a standardized and operator-independent manner. We propose here a novel image processing tool to quantify the protein aggregates in a Caenorhabditis elegans (C. elegans) model of MJD. Confocal mi-croscopy images were obtained from animals of different genetic conditions. The image processing application was developed using MeVisLab as a platform to pro-cess, analyse and visualize the images obtained from those animals. All segmenta-tion algorithms were based on intensity pixel levels.The quantification of area or numbers of aggregates per total body area, as well as the number of aggregates per animal were shown to be reliable and reproducible measures of protein aggrega-tion in C. elegans. The results obtained were consistent with the levels of aggrega-tion observed in the images. In conclusion, this novel imaging processing applica-tion allows the non-biased, reliable and high throughput quantification of protein aggregates in a C. elegans model of MJD, which may contribute to a significant improvement on the prognosis of treatment effectiveness for this group of disor-ders

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Color model representation allows characterizing in a quantitative manner, any defined color spectrum of visible light, i.e. with a wavelength between 400nm and 700nm. To accomplish that, each model, or color space, is associated with a function that allows mapping the spectral power distribution of the visible electromagnetic radiation, in a space defined by a set of discrete values that quantify the color components composing the model. Some color spaces are sensitive to changes in lighting conditions. Others assure the preservation of certain chromatic features, remaining immune to these changes. Therefore, it becomes necessary to identify the strengths and weaknesses of each model in order to justify the adoption of color spaces in image processing and analysis techniques. This chapter will address the topic of digital imaging, main standards and formats. Next we will set the mathematical model of the image acquisition sensor response, which enables assessment of the various color spaces, with the aim of determining their invariance to illumination changes.