Automatic In Situ Identification of Plankton

Blaschko, Matthew B.; Holness, Gary; Mattar, Marwan; Lisin, Dimitri; Utgoff, Paul E.; Hanson, Allen R.; Schultz, Howard; Riseman, Edward M.

doi:10.1109/acvmot.2005.29

Cited by 61 publications

(37 citation statements)

References 25 publications

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“…The performance of our automated classifier exceeds that expected for consistency between manual microscopists (Culverhouse et al 2003) or achieved with other automated applications to plankton images (e.g., Culverhouse et al 2003;Grosjean et al 2004;Blaschko et al 2005;Hu and Davis 2005;Luo et al 2005). The approach provides unbiased quantitative abundance estimates (and associated uncertainty estimates) with taxonomic resolution similar to many applications of manual microscopic analysis to plankton samples.…”

Section: Discussionmentioning

confidence: 79%

“…Classifier design and training-For our multicategory classification problem, we use a support vector machine (SVM), a supervised learning method that is typically easier to use than neural networks and is proving popular for a variety of classification problems, including those involving plankton (Luo et al 2004;Blaschko et al 2005;Hu and Davis 2005). SVM algorithms are based on maximizing margins separating categories in multidimensional feature space.…”

Section: Materials and Proceduresmentioning

confidence: 99%

“…These examples from previous work point to the utility of automated image processing and classification techniques for problems in phytoplankton identification, but they all address a relatively narrow scope in terms of taxonomic range or image type (e.g., cleaned frustules). Blaschko et al (2005) highlighted the challenges of moving beyond this level by presenting results with ~50% to 70% accuracy for a 12-category (plus "unknown") image classification problem involving a variety of phytoplankton groups.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated taxonomic classification of phytoplankton sampled with imaging‐in‐flow cytometry

Sosik

Olson

2007

Limnology & Ocean Methods

385

327

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High-resolution photomicrographs of phytoplankton cells and chains can now be acquired with imagingin-flow systems at rates that make manual identification impractical for many applications. To address the challenge for automated taxonomic identification of images generated by our custom-built submersible Imaging FlowCytobot, we developed an approach that relies on extraction of image features, which are then presented to a machine learning algorithm for classification. Our approach uses a combination of image feature types including size, shape, symmetry, and texture characteristics, plus orientation invariant moments, diffraction pattern sampling, and co-occurrence matrix statistics. Some of these features required preprocessing with image analysis techniques including edge detection after phase congruency calculations, morphological operations, boundary representation and simplification, and rotation. For the machine learning strategy, we developed an approach that combines a feature selection algorithm and use of a support vector machine specified with a rigorous parameter selection and training approach. After training, a 22-category classifier provides 88% overall accuracy for an independent test set, with individual category accuracies ranging from 68% to 99%. We demonstrate application of this classifier to a nearly uninterrupted 2-month time series of images acquired in Woods Hole Harbor, including use of statistical error correction to derive quantitative concentration estimates, which are shown to be unbiased with respect to manual estimates for random subsamples. Our approach, which provides taxonomically resolved estimates of phytoplankton abundance with fine temporal resolution (hours for many species), permits access to scales of variability from tidal to seasonal and longer.

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Section: Discussionmentioning

confidence: 79%

Section: Materials and Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated taxonomic classification of phytoplankton sampled with imaging‐in‐flow cytometry

Sosik

Olson

2007

Limnology & Ocean Methods

385

327

View full text Add to dashboard Cite

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“…A set of suitable features for training the MLP was selected from Fourier descriptors, geometrical features, and features characterizing the gray level distribution in an image region. Blaschko et al [27] achieved 50% -70% classification accuracy in a task of phytoplankton categorization into 12 classes plus an "unknown" class. Various shape features, moments, texture features, and contour features (780 features in total) were used.…”

mentioning

confidence: 99%

An Integrated Approach to Analysis of Phytoplankton Images

Verikas

Gelžinis

Bačauskienė

et al. 2015

IEEE J. Oceanic Eng.

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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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“…We have also used two kinds of global texture features: local binary patterns (LBP), which are gray-scale and rotation invariant texture operators [13], and shape index which is computed using the isophote and the flowline curvatures of the intensity surface [14]. These features comprise an effective subset of the features explored for plankton categorization in [1]. Classification results for several commonly used classifiers are shown in Figure 2.…”

Section: Classification With Global Featuresmentioning

confidence: 99%