Region Growing for Segmenting Green Microalgae Ima... - BV FAPESP
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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Region Growing for Segmenting Green Microalgae Images

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Author(s):
Borges, Vinicius R. P. [1] ; de Oliveira, Maria Cristina F. [1] ; Silva, Thais Garcia [2] ; Henriques Vieira, Armando Augusto [2] ; Hamann, Bernd [3]
Total Authors: 5
Affiliation:
[1] Univ Sao Paulo, Inst Ciencias Matemat & Computacao, BR-13565905 Sao Carlos, SP - Brazil
[2] Univ Fed Sao Carlos, Dept Bot, BR-13565905 Sao Carlos, SP - Brazil
[3] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 - USA
Total Affiliations: 3
Document type: Journal article
Source: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS; v. 15, n. 1, p. 257-270, JAN-FEB 2018.
Web of Science Citations: 1
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. (AU)

FAPESP's process: 11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications
Grantee:Luis Gustavo Nonato
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 13/26647-0 - Visual exploration to support green algae taxonomic classification
Grantee:Vinícius Ruela Pereira Borges
Support Opportunities: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 12/00269-7 - Visual exploration of feature spaces to support Green Algae Taxonomic Classification
Grantee:Vinícius Ruela Pereira Borges
Support Opportunities: Scholarships in Brazil - Doctorate