(Semi-) Automatic Recognition of Microorganisms in Water K. Rodenacker, P. Gais 2, U. Jütting and B. A. Hense GSF-IBB, 2 GSF-Patho, Neuherberg, Germany.

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Automatic Classification of Plankton from Digital Images

Presentation transcript:

(Semi-) Automatic Recognition of Microorganisms in Water K. Rodenacker, P. Gais 2, U. Jütting and B. A. Hense GSF-IBB, 2 GSF-Patho, Neuherberg, Germany

Karsten Rodenacker GSF-IBB AG2 2 Content Introduction Material Methods Results Summary and Discussion

Karsten Rodenacker GSF-IBB AG2 3 Introduction Studies on the effects of toxicants on the biocenosis of aquatic model ecosystem Characterization of plankton commun. (identification, counting of phyto- plankton)

Karsten Rodenacker GSF-IBB AG2 4 Material Preparation Container of semi- permeable LDPE tubes containing the test substance Water column

Karsten Rodenacker GSF-IBB AG2 5 Material Data gathering Sedimentation Slide preparation Microscopy

Karsten Rodenacker GSF-IBB AG2 6 Material Data processing with QWin, QUIPS Scan path and autofocus Digitization and storage ~45 sec/image

Karsten Rodenacker GSF-IBB AG2 7 Material Some organisms to be classified GYLAPEUMMIPUCRER HUKUOOMACHACCRMA QULAKICOZIGAACMX CLSAPLGEZIGA

Karsten Rodenacker GSF-IBB AG2 8 Methods Data processing with IDL Image segmentation Feature extraction Classification Re-classification and training

Karsten Rodenacker GSF-IBB AG2 9 Methods Image segmentation: (two-step method) Rough segmentation image threshold Fine segmentation object threshold (RATS) Unbiased count (forbidden line)

Karsten Rodenacker GSF-IBB AG2 10 Methods Feature extraction Shape geometrical analytical (Fourier, curvature) topological (convex hull, distance map) algebraic (moments, PCA)

Karsten Rodenacker GSF-IBB AG2 11 Methods Feature extraction Extinction (optical density) histogram features mean (M1), SD (M2), skewness (M3) etc. moments (algebraic) transmitted light optical density Histogram of values of transmitted light

Karsten Rodenacker GSF-IBB AG2 12 Methods Classification Hierarchical tree classifier based on stepwise linear discriminance analysis ALL GYLA KICO OOMA CLSA CHAC CRER PEUM Artefacts

Karsten Rodenacker GSF-IBB AG2 13 Methods Re-Classification Interaction Control Correction Training

Karsten Rodenacker GSF-IBB AG2 14 Preliminary Results Comparison of manual and automatic procedure

Karsten Rodenacker GSF-IBB AG2 15 Preliminary Results Comparison of manual and automatic procedure

Karsten Rodenacker GSF-IBB AG2 16 Summary and Discussion Difficulties or failures Separate softwares (Qwin, IDL) Autofocus automized microscope Segmentation Unlimited number of organism groups

Karsten Rodenacker GSF-IBB AG2 17 Summary and Discussion Successes Effective training system for biologists AND computer scientists Very good collaboration between the different faculties

Karsten Rodenacker GSF-IBB AG2 18 Summary and Discussion Outlook Fluorescence Multiple focal depth Type specific object shape features (dominant feature points) Texture object features