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

## Presentation on theme: "Automatic determination of skeletal age from hand radiographs of children Image Science Institute Utrecht University C.A.Maas."— Presentation transcript:

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 –Greulich and Pyle Motivation Development of the hand Estimating the skeletal age –Tanner and Whitehouse

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

Preprocessing operations Rotation Framing upside-down check

Rotation Radiograph Gradient Histogram -30° 60°

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

Algorithm

Results Rotation  99% Framing –Vertical  92% –Horizontal  79% upside down  100%

Segmentation of the hand Statistical Shape Model of the hand Manual segmentation –49 fixed landmark points –66 intermediate points Represent shape by vector x = (x 1,y 1,x 2,y 2,….x 115,y 115 ) N=100

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

Model variations 99% of shapes represented by 13 modes

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

Demonstration of ASM

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

Results Starting position: average distance Average shape27.5 pixels Best starting position11.0 pixels Segmentation: goodmoderately-moderately-bad goodbad 77%15%4%4%

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

Staging of radius E G H I F Rotate Translate Scale

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

Extension 2: Comparison Average image reference images –12 reference images per stage

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

Reclassification Confusion matrix BCDEFGHI B20010000 C02000000 D00500000 E002134110 F0001326210 G0001221620 H000005430 I000000179 62% similar classified97% within one stage difference

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

Results (2/2) Regioncomparisoncorrectwithin one classifiedstage error Boxaverage39%89% Boxreference46%95% 17 circlesaverage58%98% 17 circlesreference× × Second observer62%97%

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

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

END

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