ASU MAT 591: Opportunities In Industry!

1. ASU MAT 591: Opportunities In Industry!

2. Special Processing Algorithms – Part 1Automatic Target RecognitionNovember 4, 2003 Chung-Fu Chang, Ph.D. Lockheed Martin Management and Data Systems ISR Systems 1300 S. Litchfield Road Goodyear Arizona

3. Agenda • Introduction • Bayesian neural network • Applications • Fingerprint classification • Medical imagery orientation • Discussions

4. Introduction • Why do we need ATR? • Too much information, not enough personnel • Who are the users? • Image analysts, fingerprint experts, radiologists, etc. • What is the underlining technologies? • Rule-based (AI, Expert Systems, etc.) • Computer vision (Pattern matching) • Statistical approach (Bayesian classifier) • Neural Network • Feedback learning • How to train the machine/algorithm? • Supervised learning • Unsupervised learning

5. Bayes Rule • is a posteriori probability • is a priori probability • The key is to closely estimate the probability density function, .

6. Example of Two Object Types Object 1 Object 2 Probability Density Function x FeatureMeasurement

7. Automatic Target Recognition • Synthetic Aperture Radar (SAR) • Signatures with statistical variation • Target representation • Target environment • Optimal approach • Bayesian classification • Neural Network paradigm

8. Overview of ATR Algorithm

9. Overview of ATR process

10. SAR Feature Investigated Mean Edge Magnitude Standard Deviation Peak Side lobe Spatial information Impulse response Polarization Height

11. Neural Network Paradigm

12. Feature Distribution Estimation f2 f2 f1 f1

13. BNN Equations

14. BNN Equations - continued

15. BNN Equations - continued

16. BNN Equations - continued

17. BNN Paradigm Likelihood One Cluster Cluster Nodes Object Type Nodes Functions p ( f | O ) P( O ) K K Feature Components . . . All the cluster nodes representing clusters which belong to an object type will connect to the node representing that object type . . .

18. F F F 1 3 2 jk s jk m , jk k b c i i j i BNN Implementation Feature Components Cluster Nodes Object Type Nodes Likelihood Functions L ( f | O ) k nn . . . All the cluster nodes representing clusters which belong to an object type will connect to the node representing that object type . . . One Cluster

19. Training and Learning

20. Pr ( O1 ) f f f Pr ( O2 ) f Pr ( O3 ) a posteriori Probability & Decision BNN Feature Vector a posteriori probabilities OK * = Bayesian Minimal Error Decision If Pr (OK*/ f ) = Pr (OK/ f ) MAX K

21. Fingerprint Classification

22. Fingerprint Basic Patterns

23. Fingerprint Classification Process

24. Prescreening Detection core core delta delta

25. Prescreening Results

26. Feature Measurements

27. Local Area Classification

28. Local Area Classification - continued

29. Merge Results

30. Decision Rules

31. Radiology Image Orientation Processing for Workstation Display Chung-Fu Chang, Kermit Hu, and Dennis L. Wilson SPIE Medical Imaging 1998, San Diego CA 23 February 1998

32. Topics • Introduction • Purpose • Nomenclature • Approach • Chest front and side • Abdomen • Results/Performance • Conclusions

33. Purpose • Integrate into the Picture Archiving and Communication Systems (PACS) • Provide an end-to-end system to support telemedicine • Enhance the productivity of radiology technicians • Reduce hospital operating cost

34. Radiology ImageOrientation Processing (RIOP) • Automatically verify the body types, and identify image orientation • Output image orientation • Chest images - two-letter code • Abdominal images - one-letter code

35. Patient Orientation for Chest Front Images RF LF RH LH HR HL FR FL

36. Patient Orientation forChest Side Images PF AF AH PH

37. Patient Orientation forAbdominal Images F H

38. Approach • Decision tree classifier • Features measured from original or segmented images • Bayesian Neural Network will be applicable, if the available data become adequate

39. Chest Image Read digital image Spatial-variant image segmentation Side (Lateral) Front (PA) Chest front or side test Side image Front image orientation orientation Unknown determination determination Lateral Notation PA Notation

40. Spatial-Variant Image Segmentation 1 For each sub-region, compute a threshold for image segmentation 2 3 4 5 6 7 8 9 10 11 12 Histogram 13 14 15 16 Ti

41. Chest Front Image Find side with maximum occupancyof image Occupancy >65% Y N Dynamically Segment Image Determine where heart is Y Detect head;1 and only 1 Rotateimage Y N Detect lungs LH,RH,LF,RF, HL,HR,FL,FR N Unknown

42. Chest Front Image Original Spatial-Variant Segmented Image Desired Orientation

43. Dynamic Image Segmentation 1 3 Threshold increases to ensure good definition of inner boundary of 2 lungs 2 4 Lockheed Martin Proprietary

44. Chest Side Image Segmented image Unknown N If patient orientation can be determined Y PF, AF, PH, AH, Boundaries of side image HA, HP, FA or FP Neck detection Variation test N If patient orientation can be determined Calculate slope from neck up or down Y PF, AF, PH, AH, HA, HP, FA or FP

45. Chest Side Image Original Image Spatial-Variant Segmented Image

46. Abdominal Image Image segmentation # of pelvis sections detected Y Y Spine Detection Sail down Pelvis Detection Y N N Sail up Both are N’s Unknown N Pelvissections detected morein upperportion # of pelvis sections detected Y Y Pelvis Detection H N F

47. Abdominal Image Original Detections of Pelvis Desired Orientation

48. Blind Test of RIOP • Total of 445 images were collected for this test • 256 chest images • 185 chest front images • 71 chest side images • 23 abdominal images

49. Chest Front vs Side Classification Algorithm Output Front Side Total Front 183 (98.9%) 2* 185 Truth Side 6** 65 (91.5%) 71 * poor image quality and peculiar image content ** 3 horizontal image, one half image

50. Chest Front Orientation Algorithm Output UNK HL HR FL FR LH LF RH RF Total HL 1 28 1 30 HR 1 6 7 FL 1 1 FR 1 6 39 1 47 Truth LH 0 0 LF 85 7 92 RH 2 2 RF 1 3 4