1. Automatic Classification of Plankton from Digital Images
M Sieracki1, E Riseman2, W Balch1, M Benfield3, A Hanson2, C Pilskaln1, H Schultz2, C Sieracki4, P Utgoff2, M Blaschko2, G Holness2, M Mattar2, D Lisin2, B Tupper1
Bigelow Laboratory for Ocean Science1 Computer Vision Lab, U. Mass. Amherst Louisiana State University Fluid Imaging Technologies4
Marine particles, including plankton and non-living particles, play important roles in ecosystem function and material flux in the oceans. Digital imaging technology used in instruments to study these particles can rapidly produce huge archives of images that require expert interpretation. Automated methods to assist the expert interpret these images are urgently needed. We are building automatic classifier systems to work with the experts to efficiently and accurately classify images of marine particles. We will use images from in-situ camera instruments (e.g. VPR) for zooplankton and marine snow, an imaging-in-flow system (FlowCAM) for phytoplankton, and digital fluorescence microscopy for pico- and nanoplankton. Experiments were conducted using low resolution FlowCAM images of 13 classes of phytoplankton from natural communities, and a variety of image features and classifiers, including classifier ensembles. These preliminary tests yielded classification accuracy of over 70%, compared to published human expert agreement of about 80%. This indicates that automated classification will be practical to automate the majority of images. We intend to develop a probabilistic approach to particle enumeration, and to test the generality of our classifiers across instrument types.
Texture Features
Gaussian Differential
Co-occurrence
Local point features
Shape Features
Perimeter, Area,
Moments, Contour,
Convexity, Symmetry
Features
Classification Methods
K-Nearest Neighbors
Decision Trees
Naïve Bayes
Ridge Regression
Support Vector Machines
Sample
Expert Classified
Image Sets
1 Video Plankton Recorder (VPR)
Test Image Sets
Label 1
Experts manually
classify particles
2 FlowCAM Imaging-in-flow
Label 2
3 Epifluorescence Microscopy
Label 3
Conclusions
Combinations of shape and texture features performed best
Support Vector Machine classifier performed best
Best accuracy was 73%, approaching consistency rate of human experts (80%)
Preliminary Results for FlowCAM Images
Experiments
980 expert labeled FlowCAM images
780 total features
5 classifiers used
Future Work
Apply to other image types, more expert classified image sets
Automated feature selection
3D FlowCAM (dual aspect angle images)
Experiments with local image features
Software tools for experts
Ensemble Classifier Results