AS WE SEE IT* Automatic image analysis of plankton: future perspectives

Culverhouse, Phil F.; Williams, R.; Benfield, Mark C.; Flood, Per R.; Sell, Anne F.; Mazzocchi, Maria Grazia; Buttino, Isabella; Sieracki, Michael E.

doi:10.3354/meps312297

Cited by 89 publications

(54 citation statements)

References 45 publications

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“…; although we observed some Pseudocalanus in our trap samples, there were not enough to reliably make the gut fullness measurements while sorting the samples. Developments in optical methods could be employed to glean more information from the samples (Culverhouse et al 2006), and measurements can be made from preserved samples for a number of indices, but any direct chemical or metabolic measurements of individuals will necessarily limit the processing time of samples. Additionally, it is important to make comparisons between migrating zooplankton and those caught in the layers between which the zooplankters are hypothesized to be moving, further limiting processing times.…”

Section: Discussionmentioning

confidence: 99%

Trapping migrating zooplankton

Pierson

Frost

Thoreson

et al. 2009

Limnology & Ocean Methods

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We have developed a method to determine if individual zooplankton make short-duration migrations, on the order of meters to tens of meters in vertical extent, between food-rich surface water and deeper, food-poor water. To do this, we developed a zooplankton trapping system, consisting of paired traps to catch zooplankton migrating downward and an inverted ring net to catch zooplankton migrating upward. These two trap systems were deployed simultaneously with the openings at the same depth, to catch zooplankton crossing a particular depth horizon, and samples were immediately sorted to make morphological and physiological measurements of individuals. Initial tests showed that there were differences in the ability to capture upward-and downwardmigrating marine zooplankton. In addition, physiological (gut contents) and morphological (prosome length) characteristics of individual copepods differed between those moving in opposite directions. These data support the hypothesis that copepods make repeated nighttime forays between deeper, food-poor water and the nearsurface, food-rich waters. This is part of an ongoing study on the variability of this behavior in different conditions and over seasonal cycles, but the method may be applicable to other pelagic environments, to explore various ecological questions.

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Section: Discussionmentioning

confidence: 99%

Trapping migrating zooplankton

Pierson

Frost

Thoreson

et al. 2009

Limnology & Ocean Methods

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“…Finally, an overall classification accuracy of 82% is obtained. Furthermore, an insightful future perspectives is given in Culverhouse et al (2006a), and a practical instrument is designed based on the above fundamental research in Culverhouse et al (2006b).…”

Section: Original Methodsmentioning

confidence: 99%

“…Beneficial AMs can help farmers to increase the agricultural yields, for Shabtai et al (1996), Ronen et al (2002) IM 1998 Gerlach SR et al Gerlach et al (1998) IM 2001 Lee MS et al Lee et al (2001) IM 2002 Lomander A et al Lomander et al (2002) IM 2002 Park JP et al Park et al (2002) IM 2007 Lecault V et al Lecault et al (2007) IM 2011 Yu B et al Yu et al (2011) MM 1992 Reichl U et al Reichl et al (1992) MM 1994 Pichon D et al Pichon et al (1994) MM 1996 Oh B et al Oh et al (1996) MM 1997 Tamura S et al Tamura et al (1997) MM 1998 Veropoulos K et al Veropoulos et al (1998) MM 1998 Wit P et al Wit and Busscher (1998) MM 1999 Kay JW et al Kay et al (1999) MM 2000 Khutlang R et al Khutlang et al (2009Khutlang et al ( , 2010a MM 2011 Rulaningtyas R et al Rulaningtyas et al (2011) MM 2011 Pangilinan C et al Pangilinan et al (2011) MM 2011-2012Osman MK et al Osman et al (2011a, b, 2012 MM 2012 Chang J et al Chang et al (2012b) MM 2012-2016 Priya E et al Priya et al (2012), Srinivasan (2015a, b, 2016) Culverhouse et al (1994Culverhouse et al ( , 1996Culverhouse et al ( , 2000Culverhouse et al ( , 2003Culverhouse et al ( , 2006a WM 1995 Thiel S et al Thiel and Davies (1995), …”

Section: Application Domain Introductionmentioning

confidence: 99%

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

Wang

2017

Artif Intell Rev

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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-

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“…Past research on recognition from digital holographic images is extremely limited, suggesting simplistic correlation and statistical methods with limited classes [176], [178], [177], [257], [263]-but does indicate that the imagery is useful for recognition. Fortunately, there is a good selection of more appropriate research showing that plankton identi…cation is indeed possible [29], [165], [166], [234], [81], [370], [147]. Figure 5-1: Example of using Chan-Vese segmentation with a copepod that has a distinctly di¤erent grayscale level than the background.…”

Section: Automatic Identi…cation Of Planktonmentioning

confidence: 99%

Computational imaging and automated identification for aqueous environments

Loomis¹

2011

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Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi…cation with bag-of-words models and multi-stage boosting for rock…sh detection. Methods for extracting images of …sh from videos of longline operations are demonstrated.A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the e¤ects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references.Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the e¢ cacy of the prototype and algorithms. Course work at MIT and WHOI was helpful throughout this thesis. In particular, course materials and projects from Dr. Antonio Torralba (computer vision, detection, and boosting), Dr. Frédo Durand (computational imaging, segmentation, and probability), Dr. Rosalind Picard (machine learning, pattern recognition, and probability), Dr. Alan 4 Edelman (parallel computing and algorithms), and Dr. Barbastathis (optical systems) are re ‡ected directly, in several cases extended after the course to become complete sections of this thesis.The MIT Museum has provided unique outreach opportunities for the ideas presented in this thesis. Seth Riskin originally involved our lab in holography activities at the MIT Museum, then Kurt Hasselbalch later proposed and guided the development of an interactive display on plankton holography. The computational approaches I learned for the museum display led to further scienti…c development and spurred the use of GPUs for processing; Section 3.3.5 is a direct consequence, as is the majority of the data processing performed throughout this thesis.In reference to data processing, Matlab/MEX and NVIDIA's CUDA have been daily cornerstones for which I am continually thankful. Finally, mad props go to Dr. José "Pepe" Domínguez-Caballero, the most in ‡uential 5 person in my scienti…c development while at MIT. Pepe taught me digital holography and worked one-on-one with me throughout the beginning of my grad student career. He continued to be involved as a fellow holographer, a mentor, and a friend, discussing ideas, encouraging active experimentation, maintaining a curiosity about optics, and o¤ering eno...

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AS WE SEE IT* Automatic image analysis of plankton: future perspectives

Cited by 89 publications

References 45 publications

Trapping migrating zooplankton

Trapping migrating zooplankton

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

Computational imaging and automated identification for aqueous environments

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