Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography

Javidi, Bahram; Moon, Inkyu; Yeom, Seokwon; Carapezza, Edward M.

doi:10.1364/opex.13.004492

Cited by 196 publications

(99 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a serial work Javidi et al (2005Javidi et al ( , 2006aJavidi et al ( , c, d, e, f, 2010a, Moon and Javidi (2005, 2006, Yeom et al ( , 2007, , a 3D multi-class classifica-tion system is developed to classify different categories of WMs with a wide set of CBMIA techniques, including image segmentation, edge detection, shape features extraction, Gabor feature extraction, feature selection, inverse Fresnel transformation, maximum likelihood estimation, statistical inference, single exposure online digital holography, graph matching and partially temporal incoherent light in-line methods. In the experiments, numerical evaluations for the system is given, where Mean-squared Distance (MSD) and Mean-absolute Distance (MAD) are used to measure the classification effectiveness, where three classes of WMs are mainly tested (100 positive and 100 negative images of each class), finally a mean MSD around 1.5% and a mean MAD around 10% are achieved.…”

Section: Original Methodsmentioning

confidence: 99%

“…In Ochoa et al (2007), a one-class classification approach is proposed to detect nematodes, where contour energy component values are encoded to represent shape information Javidi et al (2005Javidi et al ( , 2006aJavidi et al ( , c, d, e, f, 2010a, Moon and Javidi (2005, 2006, Moon et al (2009Moon et al ( , 2010, , , Yeom et al (2007) Classification results for nematode (green) and non-nematode contours (red) some nonnematode contours were removed to improve visibility (Ochoa et al 2007). (Color figure online) of nematodes, and a matching method is used to classify each of these individual SMs.…”

Section: Overview Of Sm Classificationmentioning

confidence: 99%

See 1 more Smart Citation

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

View full text Add to dashboard Cite

Microorganisms such as protozoa and bacteria play very important roles in many practical domains, like agriculture, industry and medicine. To explore functions of different categories of microorganisms is a fundamental work in biological studies, which can assist biologists and related scientists to get to know more properties, habits and characteristics of these tiny but obbligato living beings. However, taxonomy of microorganisms (microorganism classification) is traditionally investigated through morphological, chemical or physical analysis, which is time and money consuming. In order to overcome this, since the 1970s innovative content-based microscopic image analysis (CBMIA) approaches are introduced to microbiological fields. CBMIA methods classify microorganisms into different categories using multiple artificial intelligence approaches, such as machine vision, pattern recognition and machine learning algorithms. Furthermore, because CBMIA approaches are semior full-automatic computer-based methods, they are very efficient and labour cost saving, supporting a technical feasibility for microorganism classification in our current big data age. In this article, we review the development history of microorganism classification using CBMIA approaches with two crossed pipelines. In the first pipeline, all related works are grouped by their corresponding microorganism application domains. By this pipeline, it is easy for microbiologists to have an insight into each special application domain and find their interested applied CBMIA techniques. In the second pipeline, the related works in each application domain are reviewed by time periods. Using this pipeline, computer scientists can see the dynamic of technological development clearly and keep up with the future devel-

show abstract

Section: Original Methodsmentioning

confidence: 99%

Section: Overview Of Sm Classificationmentioning

confidence: 99%

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Wang

2017

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Yeom et al [102,31] use Gabor filtering on voxelized objects. The resulting features are matched using a dynamic link association which is composed of rigid motion of a graph followed by elastic deformation.…”

Section: Spectral Methodsmentioning

confidence: 99%

Three-dimensional object registration using wavelet features

Chalfant

Patrikalakis

2009

Engineering with Computers

View full text Add to dashboard Cite

Recent developments in shape-based modeling and data acquisition have brought three-dimensional models to the forefront of computer graphics and visualization research. New data acquisition methods are producing large numbers of models in a variety of fields. Three-dimensional shape-based matching and registration (alignment) are key to the useful application of such models in areas from automated surface inspection to cancer detection and surgery. The three-dimensional models in these applications are typically huge. State-of-the-art simulations in computational fluid dynamics produce upward of four terabytes of data per second of flow. Research-level magnetic resonance imaging (MRI) resolutions can reach 1 cubic micro-meter. As a result, object registration and matching algorithms must handle very large amounts of data.The algorithms developed in this thesis accomplish automatic registration and matching of three-dimensional voxelized models. We employ features in a wavelet transform domain to accomplish registration. The features are extracted in a multiresolutional format, thus delineating features at various scales for robust and rapid matching. Registration is achieved through seeking peaks in sets of rotation quaternions using a voting scheme, then separately identifying translation. The method is robust to occlusion, clutter and noise. The efficacy of the algorithm is demonstrated through examples from solid modeling and medical imaging applications.

show abstract

“…1. Using digital in-line holographic microscopy or digital holographic microscopy based on Mach-Zehnder interferometry, the shapes, sizes, and 3-D positions of living organelles were measured [14][15][16][17][18][19][20][21][22]. However, previous works have only focused on the 2-D imaging of phytoplankton and 3-D internal structures of individual live phytoplankton have not been measured yet, largely owing to the limitation of tomographic capability of conventional in-line holography or digital holographic microscopy.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution 3-D Refractive Index Tomography and 2-D Synthetic Aperture Imaging of Live Phytoplankton

Lee¹,

K²,

Mubarok³

et al. 2014

Journal of the Optical Society of Korea

View full text Add to dashboard Cite

Optical measurements of the morphological and biochemical imaging of phytoplankton are presented. Employing quantitative phase imaging techniques, 3-D refractive index maps and high-resolution 2-D quantitative phase images of individual live phytoplankton are simultaneously obtained without exogenous labeling agents. In addition, biochemical information of individual phytoplankton including volume, mass, and density of individual phytoplankton are also quantitatively obtained from the measured refractive index distributions. We expect the present method to become a powerful tool for the study of phytoplankton.

show abstract

Three-dimensional imaging and recognition of microorganism using single-exposure on-line (SEOL) digital holography

Cited by 196 publications

References 0 publications

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

A survey for the applications of content-based microscopic image analysis in microorganism classification domains

Three-dimensional object registration using wavelet features

High-Resolution 3-D Refractive Index Tomography and 2-D Synthetic Aperture Imaging of Live Phytoplankton

Contact Info

Product

Resources

About