Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/35275
Title: Region growing for segmenting green microalgae images
Keywords: Seeded region growing
Freshwater green microalgae
Image segmentation
Gaussian distribution
Crescimento da região semeada
Microalgas verdes de água doce
Segmentação de imagens
Distribuição gaussiana
Issue Date: Jan-2018
Publisher: Association for Computing Machinery
Citation: BORGES, V. R. P. Region growing for segmenting green microalgae images. Transactions on Computational Biology and Bioinformatics, New York, v. 15, n. 1, p. 257-270, Jan./Feb. 2018.
Abstract: We describe a specialized methodology for segmenting 2D microscopy digital images of freshwater green microalgae. The goal is to obtain representative algae shapes to extract morphological features to be employed in a posterior step of taxonomical classification of the species. The proposed methodology relies on the seeded region growing principle and on a fine-tuned filtering preprocessing stage to smooth the input image. A contrast enhancement process then takes place to highlight algae regions on a binary pre-segmentation image. This binary image is also employed to determine where to place the seed points and to estimate the statistical probability distributions that characterize the target regions, i.e., the algae areas and the background, respectively. These preliminary stages produce the required information to set the homogeneity criterion for region growing. We evaluate the proposed methodology by comparing its resulting segmentations with a set of corresponding ground-truth segmentations (provided by an expert biologist) and also with segmentations obtained with existing strategies. The experimental results show that our solution achieves highly accurate segmentation rates with greater efficiency, as compared with the performance of standard segmentation approaches and with an alternative previous solution, based on level-sets, also specialized to handle this particular problem.
URI: https://dl.acm.org/citation.cfm?id=3186447
http://repositorio.ufla.br/jspui/handle/1/35275
Appears in Collections:DCC - Artigos publicados em periódicos

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