Skip to Main content Skip to Navigation
Conference papers

Probabilistic Modeling of Landmark Distances and Structure for Anomaly-proof Landmark Detection

Abstract : A combinatorial optimization algorithm for detecting multiple anatomical landmarks is presented. It can determine the positions of over 100 landmarks concurrently, taking spatial correlations of all landmark pairs into account. Provided that a set of landmark candidate lists is given by sensitivity-optimized single-landmark detectors, the proposed algorithm can find the most probable combination of them through solving a MAP estimation-based combinatorial optimization problem. Additionally, it is designed to handle subjects with "segmentation anomaly of the spinal column," a common anatomical anomaly of the spine. The proposed system was evaluated with 156 landmarks in 50 datasets, using virtually created detector output sets. In the result, the algorithm achieved 97.6\% of spinal anomaly estimation accuracy even with 50 points of candidates given per landmark, as well as 96.2\% of accuracy in landmark candidate selection. From these results, usefulness of the proposed algorithm for subjects with spinal anomaly was suggested.
Document type :
Conference papers
Complete list of metadata

Cited literature [5 references]  Display  Hide  Download

https://hal.inria.fr/inria-00624109
Contributor : Xavier Pennec Connect in order to contact the contributor
Submitted on : Thursday, September 15, 2011 - 5:18:58 PM
Last modification on : Monday, May 17, 2021 - 12:00:04 PM

File

MFCA11_P_3.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : inria-00624109, version 1

Collections

Citation

Shouhei Hanaoka, yoshitaka Masutani, Mitsutaka Nemoto, yukihiro Nomura, Takeharu yoshikawa, et al.. Probabilistic Modeling of Landmark Distances and Structure for Anomaly-proof Landmark Detection. Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability, Sep 2011, Toronto, Canada. pp.159-169. ⟨inria-00624109⟩

Share

Metrics

Record views

126

Files downloads

150