%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2015/06.24.22.18
%2 sid.inpe.br/sibgrapi/2015/06.24.22.18.36
%@doi 10.1109/SIBGRAPI.2015.33
%T A Highly Accurate Level Set Approach for Segmenting Green Microalgae Images
%D 2015
%A Borges, Vinicius Ruela Pereira,
%A Hamman, Bernd,
%A Silva, Thais Garcia,
%A Vieira, Armando Augusto Henriques,
%A Oliveira, Maria Cristina Ferreira de,
%@affiliation University of Sao Paulo
%@affiliation University of California, Davis
%@affiliation Federal University of Sao Carlos
%@affiliation Federal University of Sao Carlos
%@affiliation University of Sao Paulo
%E Papa, Joćo Paulo,
%E Sander, Pedro Vieira,
%E Marroquim, Ricardo Guerra,
%E Farrell, Ryan,
%B Conference on Graphics, Patterns and Images, 28 (SIBGRAPI)
%C Salvador, BA, Brazil
%8 26-29 Aug. 2015
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K edge detection, Gaussian distribution, green microalgae, image segmentation, level set.
%X We present a method for segmenting 2D microscopy images of freshwater green microalgae. Our approach is based on a specialized level set method, leading to efficient and highly accurate algae segmentation. The level set formulation of our problem allows us to generate an algae's boundary curve as the result of an evolving level curve, based on computed background and algae regions in a given image. By characterizing the distributions of image intensity values in local regions, we are able to automatically classify image regions into background and algae regions. We present results obtained with our method. These results are very promising as they document that we can achieve highly accurate algae segmentations when comparing ours against manually segmented images (segmented by an expert biologist) and with results derived by other approaches covered in the literature.
%@language en
%3 PID3769253.pdf