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Automated segmentation, tracking and evaluation of bacteria in microscopy images
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Orientador(es)
Resumo(s)
Most of the investigation in microbiology relies on microscope imaging and needs to be
complemented with reliable methods of computer assisted image processing, in order to avoid manual analysis.
In this work, a method to assist the study of the in vivo kinetics of protein expression
from Escherichia coli cells was developed. Confocal fluorescence microscopy (CFM) and
Differential Interference Contrast (DIC) microscopy images were acquired and processed
using the developed method. This method comprises two steps: the first one is focused
on the cells detection using DIC images. The latter aligns both DIC and CFM images and
computes the fluorescence level emitted by each cell.
For the first step, the Gradient Path Labelling (GPL) algorithm was used which produces a moderate over-segmented DIC image. The proposed algorithm, based on decision trees generated by the Classification and Regression Trees (CART) algorithm, discards the backgrounds regions and merges the regions belonging to the same cell.
To align DIC/fluorescence images an exhaustive search of the relative position and scale parameters that maximizes the fluorescence inside the cells is made. After the cells have been located on the CFM images, the fluorescence emitted by each cell is evaluated.
The discard classifier performed with an error rate of 1:81% 0:98% and the merge
classifier with 3:25% 1:37%. The segmentation algorithm detected 93:71% 2:06% of the cells in the tested images. The tracking algorithm correctly followed 64:52% 16:02% of cells and the alignment method successfully aligned all the tested images.
Descrição
Dissertação para obtenção do Grau de Mestre em
Engenharia Biomédica
Palavras-chave
Segmentation Alignment GPL Classifier CART