1. Aneurysm identification by analysis of the blood-vessel skeleton
Josef Kohout, Alessandro Chiarini, Gordon J. Clapworthy, Gregor Klajnšek 
Computer Methods and Programs in Biomedicine 
Volume 109, Issue 1, Pages (January 2013)
DOI: /j.cmpb
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2. Fig. 1
processing toolchain: from medical images, increasingly sophisticated computer models are built. As complexity increases, more predictive information is extracted, whereas the confidence decreases as additional computational steps are involved.
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3. Fig. 2 Visualisation of curvatures: Gaussian (left) and mean (right).
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4. Fig. 3
Classification of surface points – each colour represents one group. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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5. Fig. 4 Comparison of the narrowest place and the neck of aneurysm.
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6. Fig. 5 Blood vessels and their skeleton.
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7. Fig. 6 Blood vessels and their skeleton created by various method.
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8. Fig. 7
The underlying skeleton, split into two parts: one (yellow) corresponding to the healthy blood vessels, the other (green) corresponding to the aneurysm. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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9. Fig. 8 Skeleton edges and their region of support on the surface.
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10. Fig. 9
Illustration of skeleton partitioning: some surface vertices belong to the healthy blood vessel, although they are supported by the edge CP of the aneurysm part of the skeleton – see the text.
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11. Fig. 10
Illustration of the calculation of the probabilities pd(V) and ps(V, E, P) – see text. Left: the calculation of pd(V); green edges belong to the set SA, yellow edges belong to the set SB, thick edges belong to subsets EA (green) or EB (yellow) – for the sake of simplicity, there is only one edge in each subset (all other edges are considered to be too far from the point V), VA and VB are points of edges from EA and EB, respectively, that are closest to the point V. Right: the calculation of ps(V, EA, P); V′ is the point closest to the point V that lies on an edge from EA, red is the shortest path from the point V′ to the point P. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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12. Fig. 11
Aneurysm probability map without (left) and with (right) post-processing adjustment. The identified neck of the aneurysm is the red contour. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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13. Fig. 12
Aneurysm probability map without (left) and with (right) post-processing adjustment. The identified neck of the aneurysm is the red contour. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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14. Fig. 13
Aneurysm probability map with smoothing: (left) no smoothing, (centre) 10 times, (right) 100 times. The post-processing adjustment is used in all cases.
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15. Fig. 14 An example of cutting of the triangle ABC.
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16. Fig. 15
The detected aneurysm (green) and its neck (red and blue contours). The skeleton point P was chosen at a junction. Default settings were used. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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17. Fig. 16
The detected aneurysm (green) and its neck (blue). The skeleton point P was chosen on a branch. The default settings were used but the post-processing adjustment was not performed. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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18. Fig. 17
The sensitivity of the accuracy of the identification of neck (red contour) to the choice of the skeleton point P (blue ball). The post-processing adjustment was not performed. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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19. Fig. 18
Blood vessels with aneurysms excised – before and after the hole filling operation.
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20. Fig. 19 The blood vessel from Fig. 18 after the smoothing operation.
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21. Fig. 20
An illustration of the sensitivity of our method on the quality of the skeleton.
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22. Fig. 21
The result of detection of aneurysm for dataset from Fig. 20 after improving the quality of the skeleton.
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23. Fig. 22
An illustration of the sensitivity of our method on the quality of the skeleton for a complex case. Left – original corrupted skeleton and the detected aneurysm for this skeleton, right – manually edited skeleton and the corresponding detected aneurysm.
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24. Fig. 23
Comparison of aneurysms detected by our method with default options (green – above) with those extracted manually by an expert (red – below). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of the article.)
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