| 1. |
- Borga, Magnus, et al.
(author)
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Improvement in Magnetic Resonance Imaging Relating to Correction of Chemical Shift Artifact and Intensity Inhomogeneity
- 2011
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Patent (pop. science, debate, etc.)abstract
- Present invention discloses systems and methods for improvement of magnetic resonance images. Correction of a chemical shift artefact in an image acquired from a magnetic resonance imaging system is obtained by a system and a method involving iterative - compensation for the misregistration effect in an image domain. Correction of an intensity inhomogeneity in such images is obtained by a system and a method involving locating voxels corresponding to pure adipose tissue and estimating correction field from these points.
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| 2. |
- Moreno, Rodrigo, et al.
(author)
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Soft classification of trabeculae in trabecular bone
- 2011
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In: Biomedical Imaging: From Nano to Macro, 2011. - : IEEE. - 9781424441273 ; , s. 1641-1644
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Conference paper (peer-reviewed)abstract
- Classification of trabecular bone aims at discriminating different types of trabeculae. This paper proposes a method to perform a soft classification from binary 3D images. In a first step, the local structure tensor is used to estimate a membership degree of every voxel to three different classes, plate-, rod- and junction-like trabeculae. In a second step, the global structure tensor of plate-like trabeculae is compared with the local orientation of rod-like trabeculae in order to discriminate aligned from non-aligned rods. Results show that soft classification can be used for estimating independent parameters of trabecular bone for every different class, by using the classification as a weighting function.
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| 3. |
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| 4. |
- Romu, Thobias, et al.
(author)
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MANA - Multi scale adaptive normalized averaging
- 2011
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In: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. - : IEEE conference proceedings. - 9781424441280 ; , s. 361-364
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Conference paper (peer-reviewed)abstract
- It is possible to correct intensity inhomogeneity in fat–water Magnetic Resonance Imaging (MRI) by estimating a bias field based on the observed intensities of voxels classified as the pure adipose tissue. The same procedure can also be used to quantify fat volume and its distribution which opens up for new medical applications. The bias field estimation method has to be robust since pure fat voxels are irregularly located and the density varies greatly within and between image volumes. This paper introduces Multi scale Adaptive Normalized Average (MANA) that solves this problem bybasing the estimate on a scale space of weighted averages. By usingthe local certainty of the data MANA preserves details where the local data certainty is high and provides realistic values in sparse areas.
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