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Automatic determination of skeletal age from hand radiographs of children

Automatic determination of skeletal age from hand radiographs of children. Image Science Institute Utrecht University C.A.Maas. Outline. Introduction Automated procedures preprocessing operations segmentation of the hand staging of the radius Discussion Conclusion. Introduction.

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Automatic determination of skeletal age from hand radiographs of children

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  1. Automatic determination of skeletal age from hand radiographs of children Image Science Institute Utrecht University C.A.Maas

  2. Outline • Introduction • Automated procedures • preprocessing operations • segmentation of the hand • staging of the radius • Discussion • Conclusion

  3. Introduction • Motivation • Development of the hand • Estimating the skeletal age • Greulich and Pyle • Tanner and Whitehouse

  4. Project setup • Goal: • Invest possibilities for automating the skeletal age determination • Tasks: • preprocessing operations • segmentation of the hand • staging of the radius

  5. Preprocessing operations • Rotation • Framing • upside-down check

  6. Rotation • Radiograph • Gradient • Histogram -30° 60°

  7. Framing and upside down • Pixel value left and right of vertical line • Horizontal projection for average intensity

  8. Algorithm

  9. Results • Rotation 99% • Framing • Vertical  92% • Horizontal  79% • upside down  100%

  10. Segmentation of the hand • Statistical Shape Model of the hand • Manual segmentation • 49 fixed landmark points • 66 intermediate points • Represent shape by vector • x = (x1,y1,x2,y2,….x115,y115) • N=100

  11. Model variations • Shapes is points in 230-D space • Principal Component Analysis • Mean and covariance are calculated 1 1 2 2

  12. Model variations • 99% of shapes represented by 13 modes

  13. Active Shape Model • Each landmark points has its local profile • Find best fit, smallest Mahalanobis distance • Adjust model based on new positions landmark points • Iterate at different resolutions

  14. Demonstration of ASM

  15. Active Shape Model • Starting position is essential for result • Best starting shape: • Generate starting shapes • Select on Mahalanobis distance

  16. Results • Starting position: average distance Average shape 27.5 pixels Best starting position 11.0 pixels • Segmentation: good moderately- moderately- bad good bad 77% 15%4% 4%

  17. Regions of Interest • Indicate ROIs on training images • Warp pointset to average shape • Calculate average positions of ROIs • Estimate positions of ROIs based on points in average shape

  18. Staging of radius E F G H Rotate Translate Scale I

  19. Extension 1: Region • Boxshaped • Compare boxes • Landmark points • Use landmark point of ASM • Circles with diameter of 40 pixels

  20. Extension 2: Comparison • Average image • reference images • 12 reference images per stage

  21. ? Classifiers • 17 features • Linear Discriminant Classifier • k- Nearest Neighbor classifier • Leave-one-out

  22. Reclassification • Confusion matrix B C D E F G H I B 2 0 0 1 0 0 0 0 C 0 2 0 0 0 0 0 0 D 0 0 5 0 0 0 0 0 E 0 0 2 13 4 1 1 0 F 0 0 0 13 26 2 1 0 G 0 0 0 1 22 16 2 0 H 0 0 0 0 0 5 43 0 I 0 0 0 0 0 0 17 9 62% similar classified 97% within one stage difference

  23. Results (1/2) • Semi-ASM versus ASM • Select 10 features from the 17 features • kNN classifier

  24. Results (2/2) Region comparison correct within one classified stage error Box average 39% 89% Box reference 46% 95% 17 circles average 58% 98% 17 circles reference × × Second observer 62% 97%

  25. Discussion • Preprocessing operations • robustness • Segmentation of the hand • self evaluation • Staging of the Radius • Good ASM for each ROI • Further steps • combine alle techniques • staging of all ROIs

  26. Conclusion • Preprocessing operations perform good (99%) • Segmenting hand with ASM is successful (92%) • kNN classifier works good • 17 circles and reference images improve results • Computer close to human 62 %; 97 % versus 58 %; 98 % • Better training data, equal distribution

  27. END

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