Biomass quantification and 3-D topography reconstruction of microalgal biofilms using digital image processing

Asgharnejad, Hashem; Sarrafzadeh, Mohammad‐Hossein; Abhar-Shegofteh, Omid; Nazloo, Ehsan Khorshidi; Oh, Hee‐Mock

doi:10.1016/j.algal.2021.102243

Cited by 13 publications

(6 citation statements)

References 49 publications

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“…In recent years, modern detecting instruments have provided more possibilities for increasingly complex test conditions. The digital image-based modern detection methods have matured, and computer processing and image analysis have been widely used in various fields of science and technology [5,6]. At present, most researchers have analyzed the pore structure of cement-based materials using the image processing technology; however, in these studies, an abundance of information contained in images was not used effectively, except the gray level.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Guo

Wang

Zhong

et al. 2022

Case Studies in Construction Materials

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Section: Introductionmentioning

confidence: 99%

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Guo

Wang

Zhong

et al. 2022

Case Studies in Construction Materials

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“…The image analysis technique used was through colour, the red-green-blue (RGB) and grey model that allows finding a direct relationship with algal concentration, requiring only image analysis software and a digital camera (Sarrafzadeh et al, 2015a;2015b). The colour of microalgae changes significantly with increasing concentration or thickness, which is easily detectable with the naked eye; this quality allows the study of cell concentration, size distribution (thickness) and cell morphology (Höpfner et al, 2010;Asgharnejad et al, 2021). In addition, recent studies have shown that RGB analysis, together with thickness and colour variation, can determine three-dimensional topography with satisfactory accuracy from digital images of biofilms.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, recent studies have shown that RGB analysis, together with thickness and colour variation, can determine three-dimensional topography with satisfactory accuracy from digital images of biofilms. This would allow, in a very novel way, to calculate quantitative parameters, such as biomass yield and biomass thickness, at each specific point of the biofilm (Asgharnejad et al, 2021). Other applications found, such as those shown by Murphy et al (2014), give more value to the technique by colour analysis.…”

Section: Introductionmentioning

confidence: 99%

Microalgal biomass quantification from the non-invasive technique of image processing through red–green–blue (RGB) analysis

et al. 2022

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Continuous monitoring of biomass concentration in microalgae cultures is essential and one of the most important parameters to measure in this field. This study aims at digital image processing in RGB and greyscale models, being a simple and low-cost method for cell estimation. Images obtained from different photobioreactors with wastewater and at different conditions for the cultivation of Chlorella vulgaris were analyzed. The results suggested that this technique is very effective under controlled lighting conditions, in contrast to photobioreactors placed outdoors and of different design, presenting a lower linearity. The accuracy of the method could be improved with a high-quality charge-coupled device (CCD) camera. The development of efficient methods to assess biomass concentration is an important and necessary step towards large-scale microalgae cultivation. The colour analysis technique has a great potential to meet the needs of monitoring cultures in a cost-effective and automated way using simple and cheap instruments.

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Section: Introductionmentioning

confidence: 99%

Texture Analysis of The Microstructure of Internal Curing Concrete Based On Image Recognition Technology

Guo

Wei-kai

Zhong

et al. 2021

Preprint

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Mechanical properties of internal curing concrete are greatly affected by its physical properties such as water content, cementing material content, porosity, and saturation. At the micro level, such impact is finally reflected in the surface texture of its materials. In this study, the image recognition technology was used to find that the internal curing concrete samples have significant micromorphology and texture features. A texture parameter–strength model was established based on the relationship between Tamura texture parameters, gray level co-occurrence matrix (GLCM) texture parameters, and the mechanical strength. Due to the characteristics of materials and the sensitivity of parameters, not all Tamura and GLCM texture parameters can effectively characterize the texture features of internal curing concrete materials. In terms of the Tamura texture, coarseness, regularity, and directionality are effective parameters to predict the compressive strength of the internal curing concrete. In terms of the GLCM texture, energy, correlation, entropy, and contrast are effective parameters to predict the compressive strength of the internal curing concrete. Correlations between each texture parameter and compressive strength follow different laws.

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Biomass quantification and 3-D topography reconstruction of microalgal biofilms using digital image processing

Cited by 13 publications

References 49 publications

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Texture analysis of the microstructure of internal curing concrete based on image recognition technology

Microalgal biomass quantification from the non-invasive technique of image processing through red–green–blue (RGB) analysis

Texture Analysis of The Microstructure of Internal Curing Concrete Based On Image Recognition Technology

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