A Highly Accurate Level Set Approach for Segmenting Green Microalgae Images

Borges, Vinícius Ruela Pereira; Hamann, Bernd; Silva, Thais G.; Vieira, Armando Augusto Henriques; Oliveira, Maria Cristina Ferreira de

doi:10.1109/sibgrapi.2015.33

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…This robust technique discriminates green microalgae species through chemotaxonomy analysis after revealing a mass range from 0.4 to 2.0 kDa, making it a suitable and alternative method for application in algal taxonomic studies. Some efforts have been channeled towards developing computational support for taxonomical classification of green microalgae species, especially in the Selenastraceae family [80]. In this context, Borges et al [81] described a specialized methodology for segmenting 2D microscopy digital images of freshwater green microalgae.…”

Section: Bioethanol and Biomethanementioning

confidence: 99%

Advances in Microalgal Research in Brazil

Matos

2021

Braz. arch. biol. technol.

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Microalgae research has attracted interest worldwide and in order to advance algal biotechnology in Brazil, government has been funding several projects. In the last 10 years, two main funds were provided by the National Council of Scientific and Technological Development (CNPq) agency to researchers in Brazil, who study the potential uses of microalgae for biomass, bioproducts and biofuels production. These funded projects addressed aspects of algal strain identification, development of algal cultivation techniques, designing photobioreactors and raceway ponds, modeling harvesting and dewatering process, maximizing biomass and oil productivities, characterizing chemical composition with different extractions systems and determining physiochemical properties of biodiesel. This review presents the state of art of algal research conducted by Brazilian institutions. Special attention is given to the recent progress on microalgal cultivation, high-value products extracted from microalgae and potential biofuels production. This review may serve as a policy instrument for planning next steps for algal research in Brazil as well as for attracting attention from international researchers who work with microalgae and would like to pursue a future partnership on algal research with Brazilian research institutions.

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Section: Bioethanol and Biomethanementioning

confidence: 99%

Advances in Microalgal Research in Brazil

Matos

2021

Braz. arch. biol. technol.

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“…In [58], due to the importance of green microalgae for giving signs to deterioration of ecological conditions, a segmentation algorithm of green microalgae images is proposed. In this approach, eigenvalues of the image are found first, then computation of multivariant Gaussian distribution parameters for algae and background is performed, before thresholding to get a binary image.…”

Section: A Threshold Based Segmentation (Tbs)mentioning

confidence: 99%

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

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Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, regionbased, and edge-based which belong to classical methods, supervised and unsupervised machine leaningbased methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened. INDEX TERMSMicroorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.

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“…Object detection procedures using microscopy images rely on both segmentation and bounding box detection. Previous work on microorganism segmentation includes methods such as thresholding, gradient-based, deformable models and feature-based approaches (Verikas et al, 2012;Kloster et al, 2014;Borges et al, 2015;Rojas Camacho et al, 2017). At best, these methods are semi-automatic and require manual fine-tuning of parameters, which is impractical.…”

Section: Introductionmentioning

confidence: 99%