Fluorescence-assisted image analysis of freshwater microalgae

Abstract

We exploit a property of microalgae-that of their ability to autofluoresce when exposed to epifluorescence illumination-to tackle the problem of detecting and analysing microalgae in sediment samples containing complex scenes. We have added fluorescence excitation to the hardware portion of our microalgae image processing system. We quantitatively measured 120 characteristics of each object detected through fluorescence excitation, and used an optimized subset of these characteristics for later automated analysis and species classification. All specimens used for training and testing our system came from natural populations found in Lake Biwa, Japan. Without the use of fluorescence excitation, automated analysis of images containing algae specimens in sediment is near impossible. We also used fluorescence imaging to target microalgae in water samples containing large numbers of obtrusive nontargeted objects, which would otherwise slow processing speed and decrease species analysis and classification accuracy. Object drift problems associated with the necessity to use both a fluorescence and greyscale image of each microscope scene were solved using techniques such as template matching and a novel form of automated seeded region growing (SRG). Our system proved to be not only user-friendly, but also highly accurate in classifying two major genera of microalgae found in Lake Biwa-the cyanobacteria Anabaena spp. and Microcystis spp. Classification accuracy was measured to be over 97%. D

Changing Microspatial Patterns of Sulfate-Reducing Microorganisms (SRM) during Cycling of Marine Stromatolite Mats

“…The problem disappears when total areas are computed. A second limitation involves image overlap [47]. This problem affects the computation of areas in the absence of a mathematical model that would account for overlapping objects.…”

Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation

“…However, these methods are restricted in taxonomical discrimination (Franqueira et al, 2000;Steinberg et al, 1996). Digital image analysis has a greater potential for discrimination, but shortcomings have limited our attempts of integrating this method into phytoplankton research until now (Bayerand et al, 2001;Gray et al, 2002;Rines, 1999;Walker, 1999;Walker et al, 1998Walker et al, , 2002. Samples taken from natural ecosystems often contain high numbers of objects in addition to living phytoplankton cells, i.e., zooplankton, detritus, and inorganic particles.…”

Automatic identification of Scenedesmus polymorphic microalgae from microscopic images

“…Luo et al use circular shape diatoms to identify using canny filter, Fourier spectrum descriptors and Artificial Neural Networks with 94.44% of accuracy [15]. Fluorescence in microalgae was employed by Walker et al to segment using region growing, and classifying them taking advantage of algae shape, frequency domain and second order statistical properties [16]. Molesh et al got 93% of accuracy classifying five types of algae: Navicula Scenedesmus Microcystis Oscillatoria and Chroococcus in river water also using shape and texture descriptors [12].…”