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(S emi -) A utomatic R ecognition of M icroorganisms in W ater

(S emi -) A utomatic R ecognition of M icroorganisms in W ater. K . Rodenacker, P . Gais 2 , U . J ü tting and B . A. Hense GSF-IBB, 2 GSF- Path o , Neuherberg, Germany. Content. Introduction Material Methods Results Summary and Discussion. Introduction.

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(S emi -) A utomatic R ecognition of M icroorganisms in W ater

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  1. (Semi-) Automatic Recognitionof Microorganismsin Water K. Rodenacker, P. Gais2, U. Jütting and B. A. Hense GSF-IBB, 2GSF-Patho,Neuherberg, Germany

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

  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 3

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

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

  6. Material • Data processingwith QWin, QUIPS • Scan path andautofocus • Digitization and storage~45 sec/image Karsten Rodenacker GSF-IBB AG2 6

  7. GYLA PEUM MIPU CRER HUKU OOMA CHAC CRMA QULA KICO ZIGA ACMX CLSA PLGE ZIGA Material • Some organisms to be classified Karsten Rodenacker GSF-IBB AG2 7

  8. Methods • Data processing with IDL • Image segmentation • Feature extraction • Classification • Re-classificationand training Karsten Rodenacker GSF-IBB AG2 8

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

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

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

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

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

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

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

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

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

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