An Integrated Approach to Analysis of Phytoplankton Images

Abstract

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.

FlowCAM optimization: Attaining good quality images for higher taxonomic classification resolution of natural phytoplankton samples

“…Examples include the work of Gorsky et al (1989) wherein they were able to distinguish species of phytoplankton with distinct size and shape using simple geometric properties. Culverhouse et al (2003) employed neural network technique utilizing different texture and edge-based image properties in classifying dinoflagellate images and Verikas et al (2014) used support vector machine (SVM) and random forest (RF) classifiers to discriminate the HAB-causing Prorocentrum minimum. The work of Sosik and Olson (2007b) may be the most detailed multiclass categorization of phytoplankton images from Imaging FlowCytobot.…”

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

“…In Verikas et al (2015), a unary-class classification system is proposed to detect multiple phytoplankton in an image. In this work, image segmentation, 100 global and local shape features, feature selection, SVM and RF classifiers are applied.…”

Automatic plankton image classification combining multiple view features via multiple kernel learning

“…Santhi et al [ 28 ] identified algal from microscopic images by applying ANN on extracted and reduced features such as texture, shape, and object boundary. Verikas et al [ 29 ] exploited light and fluorescence microscopic images to extract geometry, shape and texture feature sets which were then selected and used in SVM as well as RF classifiers to distinguish between Prorocentrum minimum cells and other objects.…”

“…Analysis of the aforementioned methods shows the performance of plankton image classification systems based on applied features and classifiers, among which the general features, such as size, invariant moments, co-occurrence matrix, Fourier descriptor, etc., and the traditional classifiers, such as SVM, RF, ANN, etc., are most commonly used respectively [ 8 , 11 – 13 , 17 , 20 , 25 , 27 , 29 ]. However, these features usually suffer from robustness shortage and cannot represent the biomorphic characteristics of plankton well.…”