Automatic Detection of Bone Contours in X-Ray Images

1. Automatic Segmentation and Detection of Joints
in X-Ray Images
Institute of Mathematics, Mechanics and Computer Science
Southern Federal University
Alexey Mikhaylichenko, Yana Demyanenko, Elena Grushko
AIST, 2016
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2. The problem
a)
⇒
b)
(a) Initial image; (b) result of contours detecting (of knee joint)
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3. Algorithm scheme
a)
⇒
b)
⇒
c)
⇒
⇒
d)
⇒
e)
⇒
f)
(a) Computation of edge map of image; (b) computation of top binarization
threshold; (c) edge thinning; (d) computation of optimal binarization threshold;
(e) binarization; (f) chaining process
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4. Edge map
a) b) c)
(a) Sobel operator; (b) gradient vector flow field 1
; (c) result of element-wise
multiplication (gamma correction applied to images)
1Xu C., Prince J. L. Snakes, Shapes, and Gradient Vector Flow
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5. Edge thinning
a) b)
(a) Result of applying non-maximum suppression; (b) result of binarizing with
one of threshold
binarization is performed for each of thresholds
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6. Discontinuity points
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7. Chaining
Violation of condition «does not intersect already existing fragments of bounds
on image» (left) and example of removing a discontinuity (right)
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8. Chaining
Chaining algorithm for unpaired point of discontinuity
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9. Threshold computation
Process of threshold finding, T0 < .. < Ti < .. < Tn ≤ T
E* = max{Ei }i=0,n, T* — optimal threshold
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10. Comparison with Canny edge detector
a) b)
(a) Proposed method; (b) Canny edge detector with manually selected
thresholds, some of best results
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11. Experimental results
Results of applying the method to coronal and lateral projection of joint
(success — 74%)
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12. Performance
Average time a single-threaded version (top); average time a multi-threaded
version (bottom)
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13. Minuses
a) b) c)
Problems: (a), (b) false border detection (14, 2%); (c) etc. (11, 8%)
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14. Conclusion
We propose an approach for automatic bone contours detection which
does not require homogeneity of regions. The main issues are:
accurate edge fragments detection and elimination of discontinuities
between them;
the criteria for calculating numerical characteristics of the quality of
image contours detection;
the algorithm for tracing contours.
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