Métodos para segmentação nuclear e avaliação da expressão gênica em imagens sagitais de embriões de Drosophila melanogaster

Abstract

In recent years, the techniques of digital image processing have been widely used in conjunction with other areas of science, including biology. These techniques generally aim to optimize time, increase accuracy and eliminate the subjectivity of the process of simple visual analysis. This increase was motivated mainly by important advances in Computational Biology and the considerable increase of databases of biological image. In this work it is presented a computational method that uses dierent techniques of image processing. The main objective is to achieve the segmentation of cell nuclei contained in sagittal images of Drosophila melanogaster embryos. This task represents an essential step for performing several analyzes about this biological model. The proposed method is based on the estimation of curvature of the object contour, and consists in separating clusters of nuclei through segments drawn between corresponding concave points. This approach is new for this scenario, and contributes to the solution of the problem of aggregated components, recurrent feature in applications related to the segmentation of nuclei. Evaluation methods include approaches to quantify and visualize the average intensity of protein and RNA in intranuclear and cytoplasmic regions of the embryo Drosophila. The quantication method, in particular, allows to obtain the gene expression prole taken along the anterior-posterior axis of the embryo Drosophila. On the other hand, the visualization method includes a new approach for the exhibition of sagittal images, in which the cell nuclei are arranged in a linear conguration. The ecient use of evaluation methods helps the interpretation of the data. In general, methods for nuclear segmentation and evaluation of gene expression represents an essential step for performing various analyzes biological on the Drosophila melanogaster. Experiments were performed to illustrate the performance of the segmentation, when compared to other approaches found in the literature. The results showed that the proposed method is eective qualitatively and quantitatively level. The method can also be used in various applications and elds, such that involve similar congurations.