| 1. |
- Gelzinis, Adas, et al.
(författare)
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Categorizing cells in phytoplankton images
- 2011
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Ingår i: Recent Advances in Signal Processing, Computational Geometry and Systems Theory. - Athens : World Scientific and Engineering Academy and Society. - 9781618040275 - 1618040278 ; , s. 82-87
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Konferensbidrag (refereegranskat)abstract
- This article is concerned with detection of invasive species---Prorocentrum minimum (P. minimum)---in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization, image segmentation, and SVM and random forest-based classification of objects was developed to solve the task. The developed algorithms were tested using 114 images of 1280 x 960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classify 94.9% of all objects. The results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species.
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| 2. |
- Gelzinis, Adas, et al.
(författare)
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Detecting P. minimum cells in phytoplankton images
- 2011
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Ingår i: Electrical and Control Technologies : proceedings of the 6th international conference on Electrical and Control Technologies ECT 2011 / Kaunas University of Technology, IFAC Committee of National Lithuanian Organisation. - Kaunas, Lithuania : Kaunas University of Technology, Lithuania. ; , s. 61-66
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Konferensbidrag (refereegranskat)abstract
- This article is concerned with detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), - which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280x960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93,25% of the objects. The results are rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species.
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| 3. |
- Jaanus, Andres, et al.
(författare)
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Changes in phytoplankton communities along a north–south gradient in the Baltic Sea between 1990 and 2008
- 2011
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Ingår i: Boreal environment research. - 1239-6095 .- 1797-2469. ; 16, s. 191-208
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Tidskriftsartikel (refereegranskat)abstract
- Evaluation of changes in Baltic Sea phytoplankton communities has been hampered by a lack of quantitative long-term data. We investigated changes in biomass of summer (June–September) phytoplankton over the last two decades (1990–2008) along a north–south gradient in the Baltic Sea. The areas were characterized by different temperature, salinity and nutrient conditions. Thirty taxonomic groups were selected for the statistical analysis. Increases in total phytoplankton, particularly cyanobacterial, biomass were observed in the Gulfs of Bothnia and Finland. In these two areas over the study period cyanobacteria also became abundant earlier in the season, and in the Curonian Lagoon Planktothrix agardhii replaced Aphanizomenon flos-aquae as the most abundant cyanobacterium. In general, water temperature was the most influential factor affecting the summer phytoplankton communities. Our data suggest that temperature increases resulting from climate change are likely to cause basin-specific changes in the phytoplankton communities, which in turn may affect overall ecosystem functioning in the Baltic Sea.
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| 4. |
- Olenina, Irina, et al.
(författare)
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Assessing impacts of invasive phytoplankton : The Baltic Sea case
- 2010
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Ingår i: Marine Pollution Bulletin. - : Elsevier BV. - 0025-326X .- 1879-3363. ; 60:10, s. 1691-1700
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Tidskriftsartikel (refereegranskat)abstract
- There is an increasing understanding and requirement to take into account the effects of invasive alien species (IAS) in environmental quality assessments. While IAS are listed amongst the most important factors threatening marine biodiversity, information on their impacts remains unquantified, especially for phytoplankton species. This study attempts to assess the impacts of invasive alien phytoplankton in the Baltic Sea during 1980-2008. A bioinvasion impact assessment method (BPL - biopollution level index) was applied to phytoplankton monitoring data collected from eleven sub-regions of the Baltic Sea. BPL takes into account abundance and distribution range of an alien species and the magnitude of the impact on native communities, habitats and ecosystem functioning. Of the 12 alien/cryptogenic phytoplankton species recorded in the Baltic Sea only one (the dinoflagellate Prorocentrum minimum) was categorized as an IAS, causing a recognizable environmental effect.
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| 5. |
- Olenina, Irina, et al.
(författare)
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The dinoflagellate Prorocentrum cordatum at the edge of the salinity tolerance : The growth is slower but cells are larger
- 2016
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Ingår i: Estuarine, Coastal and Shelf Science. - London : Academic Press. - 0272-7714 .- 1096-0015. ; 168:5, s. 71-79
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Tidskriftsartikel (refereegranskat)abstract
- In this study we examine how the projected climate change driven decrease in the Baltic Sea salinity can impact the growth, cell size and shape of the recently invaded dinoflagellate Prorocentrum cordatum. In laboratory treatments we mimicked salinity conditions at the edge of the mesohaline south-eastern Baltic and oligohaline-to-limnic Curonian Lagoon. We used an innovative computer-based method allowing detection of P. cordatum cells and quantitative characterization of cell contours in phytoplankton images. This method also made available robust indicators of the morphometric changes, which are not accessible for an expert studying cells under light microscope. We found that the salinity tolerance limit of P. cordatum ranges between 1.8 and 3.6, and that the mean cell size of its population is inversely proportional to both salinity and nutrient content. Under ambient south-eastern Baltic salinity (7.2) the nutrients were stimulating the growth of P. cordatum; while at the edge of its salinity tolerance the nutrient availability did not have such effect. We suggest that in the future Baltic the decline insalinity and increase in nutrient loads may result in larger cells of P. cordatum and extended duration of their presence in plankton, causing longer periods of algal blooms.
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| 6. |
- Vaiciukynas, Evaldas, et al.
(författare)
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Exploiting statistical energy test for comparison of multiple groups in morphometric and chemometric data
- 2015
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Ingår i: Chemometrics and Intelligent Laboratory Systems. - Amsterdam : Elsevier. - 0169-7439 .- 1873-3239. ; 146, s. 10-23
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Tidskriftsartikel (refereegranskat)abstract
- Multivariate permutation-based energy test of equal distributions is considered here. Approach is attributable to the emerging field of ε-statistics and uses natural logarithm of Euclidean distance for within-sample and between-sample components. Result from permutations is enhanced by a tail approximation through generalized Pareto distribution to boost precision of obtained p-values. Generalization from two-sample case to multiple samples is achieved by combining p-values through meta-analysis. Several strategies of varied statistical power are possible, while a maximum of all pairwise p-values is chosen here. Proposed approach is tested on several morphometric and chemometric data sets. Each data set is additionally transformed by principal component analysis for the purpose of dimensionality reduction and visualization in 2D space. Variable selection, namely, sequential search and multi-cluster feature selection, is applied to reveal in what aspects the groups differ most.Morphometric data sets used: 1) survival data of house sparrows Passer domesticus; 2) orange and blue varieties of rock crabs Leptograpsus variegatus; 3) ontogenetic stages of trilobite species Trimerocephalus lelievrei; 4) marine phytoplankton species Prorocentrum minimum.Chemometric data sets used: 1) essential oils composition of medicinal plant Hyptis suaveolensspecimens; 2) chemical information of olive oil samples; 3) elemental composition of biomass ash; 4) exchangeable cations of earth metals in forest soil samples.Statistically significant differences between groups were successfully indicated, but the selection of variables had a profound effect on the result. Permutation-based energy test and it’s multi-sample generalization through meta-analysis proved useful as an unbalanced non-parametric MANOVA approach. Introduced solution is simple, yet flexible and powerful, and by no means is confined to morphometrics or chemometrics alone, but has a wide range of potential applications. Copyright © 2015 Elsevier B.V.
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| 7. |
- Vaiciukynas, Evaldas, et al.
(författare)
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Prototype-Based Contour Detection Applied to Segmentation of Phytoplankton Images
- 2013
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Ingår i: AWERProcedia Information Technology and Computer Science. ; , s. 1285-1292
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Konferensbidrag (refereegranskat)abstract
- Novel prototype-based framework for image segmentation is introduced and successfully applied for cell segmentation in microscopy imagery. This study is concerned with precise contour detection for objects representing the Prorocentrum minimum species in phytoplankton images. The framework requires a single object with the ground truth contour as a prototype to perform detection of the contour for the remaining objects. The level set method is chosen as a segmentation algorithm and its parameters are tuned by differential evolution. The fitness function is based on the distance between pixels near contour in the prototype image and pixels near detected contour in the target image. Pixels “of interest correspond to several concentric bands of various width in outer and inner areas, relative to the contour. Usefulness of the introduced approach was demonstrated by comparing it to the basic level set and advanced Weka segmentation techniques. Solving the parameter selection problem of the level set algorithm considerably improved segmentation accuracy.
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| 8. |
- Verikas, Antanas, 1951-, et al.
(författare)
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An Integrated Approach to Analysis of Phytoplankton Images
- 2015
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Ingår i: IEEE Journal of Oceanic Engineering. - New York, NY : IEEE Oceanic Engineering Society. - 0364-9059 .- 1558-1691. ; 40:2, s. 315-326
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Tidskriftsartikel (refereegranskat)abstract
- The main objective of this paper is detection, recognition, and abundance estimation of objects representing the Prorocentrum minimum (Pavillard) Schiller (P. minimum) species in phytoplankton images. The species is known to cause harmful blooms in many estuarine and coastal environments. The proposed technique for solving the task exploits images of two types, namely, obtained using light and fluorescence microscopy. Various image preprocessing techniques are applied to extract a variety of features characterizing P. minimum cells and cell contours. Relevant feature subsets are then selected and used in support vector machine (SVM) as well as random forest (RF) classifiers to distinguish between P. minimum cells and other objects. To improve the cell abundance estimation accuracy, classification results are corrected based on probabilities of interclass misclassification. The developed algorithms were tested using 158 phytoplankton images. There were 920 P. minimum cells in the images in total. The algorithms detected 98.1% of P. minimum cells present in the images and correctly classified 98.09% of all detected objects. The classification accuracy of detected P. minimum cells was equal to 98.9%, yielding a 97.0% overall recognition rate of P. minimum cells. The feature set used in this work has shown considerable tolerance to out-of-focus distortions. Tests of the system by phytoplankton experts in the cell abundance estimation task of P. minimum species have shown that its performance is comparable or even better than performance of phytoplankton experts exhibited in manual counting of artificial microparticles, similar to P. minimum cells. The automated system detected and correctly recognized 308 (91.1%) of 338 P. minimum cells found by experts in 65 phytoplankton images taken from new phytoplankton samples and erroneously assigned to the P. minimum class 3% of other objects. Note that, due to large variations of texture and size of P. minimum cells as well as- background, the task performed by the system was more complex than that performed by the experts when counting artificial microparticles similar to P. minimum cells.
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| 9. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Automated image analysis- and soft computing-based detection of the invasive dinoflagellate Prorocentrum minimum (Pavillard) Schiller
- 2012
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Ingår i: Expert systems with applications. - Amsterdam : Elsevier. - 0957-4174 .- 1873-6793. ; 39:5, s. 6069-6077
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Tidskriftsartikel (refereegranskat)abstract
- A long term goal of this work is an automated system for image analysis- and soft computing-based detection, recognition, and derivation of quantitative concentration estimates of different phytoplankton species using a simple imaging system. This article is limited, however, to detection of objects in phytoplankton images, especially objects representing one invasive species-Prorocentrum minimum (P. minimum), which is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects, stochastic optimization, and image segmentation was developed for solving the task. The developed algorithms were tested using 114 images of 1280 × 960 pixels size recorded by a colour camera. There were 2088 objects representing P. minimum cells in the images in total. The algorithms were able to detect 93.25% of the objects. Bearing in mind simplicity of the imaging system used the result is rather encouraging and may be applied for future development of the algorithms aimed at automated classification of objects into classes representing different phytoplankton species. © 2011 Elsevier Ltd. All rights reserved.
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| 10. |
- Verikas, Antanas, 1951-, et al.
(författare)
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Phase congruency-based detection of circular objects applied to analysis of phytoplankton images
- 2012
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Ingår i: Pattern Recognition. - Amsterdam : Elsevier. - 0031-3203 .- 1873-5142. ; 45:4, s. 1659-1670
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Tidskriftsartikel (refereegranskat)abstract
- Detection and recognition of objects representing the Prorocentrum minimum (P. minimum) species in phytoplankton images is the main objective of the article. The species is known to cause harmful blooms in many estuarine and coastal environments. A new technique, combining phase congruency-based detection of circular objects in images, stochastic optimization-based object contour determination, and SVM- as well as random forest (RF)-based classification of objects was developed to solve the task. A set of various features including a subset of new features computed from phase congruency preprocessed images was used to characterize extracted objects. The developed algorithms were tested using 114 images of 1280×960 pixels. There were 2088 P. minimum cells in the images in total. The algorithms were able to detect 93.25% of objects representing P. minimum cells and correctly classified 94.9% of all detected objects. The feature set used has shown considerable tolerance to out-of-focus distortions. The obtained results are rather encouraging and will be used to develop an automated system for obtaining abundance estimates of the species. © 2011 Elsevier Ltd All rights reserved.
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