Biomedical Image Registration: 6th International Workshop, by Sebastien Ourselin, Marc Modat

By Sebastien Ourselin, Marc Modat

This e-book constitutes the refereed complaints of the sixth foreign Workshop on Biomedical photo Registration, WBIR 2014, held in London, united kingdom, in July 2014.

The sixteen complete papers and eight poster papers integrated during this quantity have been conscientiously reviewed and chosen from a number of submitted papers. the total papers are geared up within the following topical sections: computational potency, version established regularisation, optimisation, reconstruction, interventional software and alertness particular measures of similarity.

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Additional info for Biomedical Image Registration: 6th International Workshop, WBIR 2014, London, UK, July 7-8, 2014. Proceedings

Example text

17] suggested using the interobserver segmentation error (ISE) to locally penalize the surface matching. However, this method only works for certain cases. To address the same issue, we propose a different registration algorithm to lower the registration error in a scenario that the manual segmentation is inaccurate. 2 Method Our registration cost function combines the costs of intensity similarity and surface closeness. In short, we improve the registration accuracy by providing an estimated target surface that is closer to the true segmentation.

18 µm for three unseen arthritic samples. This may suggest that pathological bone shape changes in models of RA are detectable as departures from the model statistics. 1 Introduction Rheumatoid arthritis (RA) is an autoimmune disease that affects approximately 1% of the world’s population [1]. The autoimmune response mounted by the body gives rise to chronic inflammation of the synovial joints, which can cause active destruction of cartilage and bone. Although the exact cause of RA is unknown, new therapeutic targets may be discovered by investigating genes or processes that exacerbate or ameliorate disease progression.

The active shape model (ASM) is commonly used to identify shape instances in medical image data by utilising the variability extracted from the training set by principal component analysis (PCA) [4]. In building such a model, it is necessary to establish point correspondences across the training set. This is often achieved by registering a single reference onto each sample, using algorithms such as iterative closet point (rigid) and B-spline free form deformation (non-rigid). This approach has been employed previously in building shape models of bones for the assessment of morphological variations in the primate humerus and scapula [5].

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