Fast Binary Dilation/Erosion Algorithm Using Kernel Subdivision

Narayanan, Ajay

doi:10.1007/11612704_34

Cited by 14 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(2018), we combined morphological dilation and erosion and performed them to delineate UGBs (Figure 1b). The morphological method includes opening operations composed of erosion first and dilation followed and closing operations composed of dilation followed by erosion (Narayanan, 2006). The opening operation can remove fragmented urban patches, while the closing operation can bridge gaps between urban patches.…”

Section: Methodsmentioning

confidence: 99%

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

et al. 2022

View full text Add to dashboard Cite

To limit urban sprawl and reduce the negative impacts of urban expansion, the United States has taken the lead in delimiting urban growth boundaries (UGBs), that is, setting boundaries around cities and limiting urban expansion within such boundaries (Daniels, 2001). This approach can effectively protect the cropland and natural habitat around cities (Wei & Ye, 2014), alleviating the contradiction between urban development and the conservation of resources and environment to realize sustainable development (Jiang, Chen, et al., 2016;Long et al., 2013;Tayyebi et al., 2011). In China, where the urban land area (ULA) is increasing rapidly, delimiting UGBs has

show abstract

Section: Methodsmentioning

confidence: 99%

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Since the constructed intensity and phase images are based on the spectral profile of the incident pulses (typically Gaussian shaped with ripples), we carefully normalized the profile from the images to extract the intensity and phase changes of the incident light caused only by the cells. Furthermore, classical dilation and erosion algorithms are applied to the images in order to determine the area of the cell in each image, extract the part that contains the cells, and remove the surrounding part that does not contain the cell. In order to characterize the microscope, in particular, to evaluate the spatial resolution, we used the modulation transfer function (MTF).…”

Section: Methodsmentioning

confidence: 99%

High‐throughput, label‐free, single‐cell, microalgal lipid screening by machine‐learning‐equipped optofluidic time‐stretch quantitative phase microscopy

et al. 2017

View full text Add to dashboard Cite

The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO 2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner. Here high-throughput labelfree single-cell screening of lipid-producing microalgal cells with optofluidic timestretch quantitative phase microscopy was demonstrated. In particular, Euglena gracilis, an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement), within lipid droplets was investigated. The optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch quantitative phase microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase maps of every single cell at a high throughput of 10,000 cells/s, enabling accurate cell classification without the need for fluorescent staining. Specifically, the dataset was used to characterize heterogeneous populations of E. gracilis cells under two different culture conditions (nitrogen-sufficient and nitrogen-deficient) and achieve the cell classification with an error rate of only 2.15%. The method holds promise as an effective analytical tool for microalgae-based biofuel production. V C 2017 International Society for Advancement of Cytometry

show abstract

“…gracilis cells with a clear background. Finally, the classical dilation and erosion algorithm [24] is applied to the images in order to remove the background and leave only the effective parts of the E . gracilis cells.…”

Section: Methodsmentioning

confidence: 99%

High-Throughput Accurate Single-Cell Screening of Euglena gracilis with Fluorescence-Assisted Optofluidic Time-Stretch Microscopy

et al. 2016

View full text Add to dashboard Cite

The development of reliable, sustainable, and economical sources of alternative fuels is an important, but challenging goal for the world. As an alternative to liquid fossil fuels, algal biofuel is expected to play a key role in alleviating global warming since algae absorb atmospheric CO2 via photosynthesis. Among various algae for fuel production, Euglena gracilis is an attractive microalgal species as it is known to produce wax ester (good for biodiesel and aviation fuel) within lipid droplets. To date, while there exist many techniques for inducing microalgal cells to produce and accumulate lipid with high efficiency, few analytical methods are available for characterizing a population of such lipid-accumulated microalgae including E. gracilis with high throughout, high accuracy, and single-cell resolution simultaneously. Here we demonstrate high-throughput, high-accuracy, single-cell screening of E. gracilis with fluorescence-assisted optofluidic time-stretch microscopy–a method that combines the strengths of microfluidic cell focusing, optical time-stretch microscopy, and fluorescence detection used in conventional flow cytometry. Specifically, our fluorescence-assisted optofluidic time-stretch microscope consists of an optical time-stretch microscope and a fluorescence analyzer on top of a hydrodynamically focusing microfluidic device and can detect fluorescence from every E. gracilis cell in a population and simultaneously obtain its image with a high throughput of 10,000 cells/s. With the multi-dimensional information acquired by the system, we classify nitrogen-sufficient (ordinary) and nitrogen-deficient (lipid-accumulated) E. gracilis cells with a low false positive rate of 1.0%. This method holds promise for evaluating cultivation techniques and selective breeding for microalgae-based biofuel production.

show abstract

Fast Binary Dilation/Erosion Algorithm Using Kernel Subdivision

Cited by 14 publications

References 5 publications

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

Delimiting China's Urban Growth Boundaries Under Localized Shared Socioeconomic Pathways and Various Urban Expansion Modes

High‐throughput, label‐free, single‐cell, microalgal lipid screening by machine‐learning‐equipped optofluidic time‐stretch quantitative phase microscopy

High-Throughput Accurate Single-Cell Screening of Euglena gracilis with Fluorescence-Assisted Optofluidic Time-Stretch Microscopy

Contact Info

Product

Resources

About