Abstract
This paper addresses the clinically challenging problem of hairline mandibular fracture detection from a sequence of Computed Tomography (CT) images. A hairline fracture of critical clinical importance, can be easily missed due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT image, if not scrutinized carefully. In this work, the 2D CT image slices displaying a mandible with hairline fractures are first identified within an input CT image sequence of a fractured craniofacial skeleton. This is achieved via an intensity-based image retrieval scheme using Kolmogorov-Smirnov distance as the measure of similarity and an unbroken mandible as the reference image. Since a hairline fracture is essentially a discontinuity in the bone contour, we model it as a minimum cut in an appropriately weighted flow network. The existing graph cut-based segmentation schemes are enhanced with a novel construction of the flow network, guided by the geometry of the human mandible. The Edmonds-Karp refinement of the classical Ford-Fulkerson algorithm is employed next to obtain a minimum cut, which represents the hairline fracture in the already identified CT image slices. Experimental results demonstrate the effectiveness of the proposed method.
| Original language | English (US) |
|---|---|
| Title of host publication | 2011 8th IEEE International Symposium on Biomedical Imaging |
| Subtitle of host publication | From Nano to Macro, ISBI'11 |
| Pages | 1962-1965 |
| Number of pages | 4 |
| DOIs | |
| State | Published - Nov 2 2011 |
| Event | 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States Duration: Mar 30 2011 → Apr 2 2011 |
Other
| Other | 2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 |
|---|---|
| Country | United States |
| City | Chicago, IL |
| Period | 3/30/11 → 4/2/11 |
Fingerprint
Keywords
- Hairline mandibular fracture
- Kolmogorov-Smirnov distance
- Max-flow mincut
ASJC Scopus subject areas
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
Cite this
Detection of hairline mandibular fracture using max-flow min-cut and Kolmogorov-Smirnov distance. / Chowdhury, Ananda S.; Bhandarkar, Suchendra M.; Robinson, Robert W.; Yu, Jack C; Liu, Tianming.
2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11. 2011. p. 1962-1965 5872794.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Detection of hairline mandibular fracture using max-flow min-cut and Kolmogorov-Smirnov distance
AU - Chowdhury, Ananda S.
AU - Bhandarkar, Suchendra M.
AU - Robinson, Robert W.
AU - Yu, Jack C
AU - Liu, Tianming
PY - 2011/11/2
Y1 - 2011/11/2
N2 - This paper addresses the clinically challenging problem of hairline mandibular fracture detection from a sequence of Computed Tomography (CT) images. A hairline fracture of critical clinical importance, can be easily missed due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT image, if not scrutinized carefully. In this work, the 2D CT image slices displaying a mandible with hairline fractures are first identified within an input CT image sequence of a fractured craniofacial skeleton. This is achieved via an intensity-based image retrieval scheme using Kolmogorov-Smirnov distance as the measure of similarity and an unbroken mandible as the reference image. Since a hairline fracture is essentially a discontinuity in the bone contour, we model it as a minimum cut in an appropriately weighted flow network. The existing graph cut-based segmentation schemes are enhanced with a novel construction of the flow network, guided by the geometry of the human mandible. The Edmonds-Karp refinement of the classical Ford-Fulkerson algorithm is employed next to obtain a minimum cut, which represents the hairline fracture in the already identified CT image slices. Experimental results demonstrate the effectiveness of the proposed method.
AB - This paper addresses the clinically challenging problem of hairline mandibular fracture detection from a sequence of Computed Tomography (CT) images. A hairline fracture of critical clinical importance, can be easily missed due to the absence of sharp surface and contour discontinuities and the presence of intensity inhomogeneity in the CT image, if not scrutinized carefully. In this work, the 2D CT image slices displaying a mandible with hairline fractures are first identified within an input CT image sequence of a fractured craniofacial skeleton. This is achieved via an intensity-based image retrieval scheme using Kolmogorov-Smirnov distance as the measure of similarity and an unbroken mandible as the reference image. Since a hairline fracture is essentially a discontinuity in the bone contour, we model it as a minimum cut in an appropriately weighted flow network. The existing graph cut-based segmentation schemes are enhanced with a novel construction of the flow network, guided by the geometry of the human mandible. The Edmonds-Karp refinement of the classical Ford-Fulkerson algorithm is employed next to obtain a minimum cut, which represents the hairline fracture in the already identified CT image slices. Experimental results demonstrate the effectiveness of the proposed method.
KW - Hairline mandibular fracture
KW - Kolmogorov-Smirnov distance
KW - Max-flow mincut
UR - http://www.scopus.com/inward/record.url?scp=80055062108&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80055062108&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2011.5872794
DO - 10.1109/ISBI.2011.5872794
M3 - Conference contribution
SN - 9781424441280
SP - 1962
EP - 1965
BT - 2011 8th IEEE International Symposium on Biomedical Imaging
ER -