1. Automatic Image Classification for the Urinoculture Screening Ing. Paolo Andreini Ing. Simone Bonechi DIISM  University of Siena December 11th, 2015

2. Urine Culture Standard Protocol Plate Analysis Sample Collection Seeding Incubation

3. Possible Advantages Better Accuracy Reduced Costs-Time Action Required Just in Case of Error Results Later Available

4. Main Goals Colony Strain Classification Bacterial Count ROI Extraction Acquisition Device

5. Algorithm Pipeline Colonies Strain Classification Bacterial Count Plate Detection Background Subtraction Acquisition

6. Plate Detection Frame Differencing Hough Transform Live Capture Motion Detection Plate Detection Saved Image

7. Uriselect 4 Chromogenic Medium Non Selective Opaque Note: our samples have been sown manually

8. Background Extraction Background Model Background Subtraction Meanshift Segmentation CIE-Lab Color Space To Compensate for Local Background Dishomogeneities

9. Yellow Colonies Effect of the Base Algorithm Effect of the Local Feature Addition Original Image

10. Classification Stage Chromogenic Substrate - Uriselect 4 ColiFaecalisKes groupS. Agalactiae ProteusPseudomonasS. AureusCandida

11. Pre-Classification Red Blue Yellow

12. Multistage Classification Pre Classification Yellow Colonies Classifier Allow to Distinguish Between the Three Main Groups Blue Colonies Classifier Allow to Distinguish between the Three Main Groups Red Colonies can be just recognized

13. FeatureExtraction Background Subtraction Meanshift Segmentation a,b (CIE-Lab) Original Image To Compensate for Local Dishomogeneities

14. Pre Classification Architecture

15. Pre-ClassificationResults Analysed Segments Number13292 Correctly Classified1326999.827 % Errors230.173 % (MLP Structure 2  6  3) MLP multilayer perceptron

16. Blue Colonies' Classification Background Subtraction Pre Classification “Sure” Background Blue colonies + Background GrabCut Algorithm

17. Blue Colonies' Classification GrabCut Algorithm Effect Background Subtraction Effect Original Image

18. Blue Colonies Classification Results Analysed Segments Number7122 Correctly Classified588882.6734 % Errors123417.3266 % MLP multilayer perceptron (MLP Structure 2  3  6  3)

19. Bacterial Count Expressed in UFC/ml (Number of Microorganisms per Milliliter of Urine) The Evaluation Scale is Logarithmic Represents an Estimation of the Infection Severity

20. Single Colonies Detection Mask Foreground Single Colonies Min Enclosing Circle for each Connected Component th(Circle Area/ Component Area)

21. Slightly Overlapping Colonies Searching for “seeds” Ellipse SelectionResult Selection Based on a Score Matrix Concavity/Convexity of Contour Estimation

22. CandidaRecognition Original Petri Plate Edge Detection Colonies Detection Searching for Not Overlapping Colonies Based on Sobel Operator

23. ChromID CPS Chromogenic Medium Non Selective Semi transparent Note: the samples have been sown automatically

24. Automatic Seeding BioMérieux PREVI™ Isola Samples are spread circularly Noise Elements on plate

25. Circular Seeding

26. Pre Processing Pre-Processing Written text Removal Label Removal ROI estraction

27. Written Text Removal Color Enhance Color Model Generalized Hough transform Post processing Sobel based Edge detection Template model Selection by: Rotation Position Dimension

28. Written Text Removal Source ImageText Removed

29. Written Text Removal Text can be occluded, is it useful to find it in this case?

30. Written Tet Removal Results Total Number of Image499 Correctly Identify 433 Errors 66 Accuracy 86,78% Accuracy in Infected Plate: 75,45% (160/212) Accuracy in non Infected Plate: 95,12% (273/287)

31. LabelRemoval Morphological FilteringMin Enclosing Rectangle Otsu Thresholding (find two distribution)

32. Anonimize the Plate Blur the patient's name for privacy reasons

33. Label Removal Results Total Number of Image499 Correctly Identify 499 Errors 0 Accuracy 100%

34. BackgroundRemoval The culture ground appearance is modeled by MOG

35. Infection Severity Estimation Max ConcentrationImage is Divided in SectorsPre-Processing

36. Infection Severity Estimation Probe the image counter-clockwise The spread angle gives the estimation

37. Infection Severity Estimation Results Total Number of Image499 Correctly Identify 477 Errors 22 Accuracy 95,4% Positive – Negative Classification Results PositiveNegative 212 22 0 265 Confusion Matrix

38. Infection Severity Estimation Results Total Number of Image212 Correctly Identify 176 Errors 36 Accuracy 83,01% Results 57 8 28 119 Confusion Matrix

39. Infection Classification The infections appearance is modeled using MOG

40. Infection Classification The image is segmented accordingly

41. Infection Classification In the uncertain regions the posterior probability is low Those regions can be ignored

42. Coming Soon Improve the segmentation performance using “local” informations

43. Coming Soon Adapt to Different Types of Culture Ground and Seeding Techniques