| 11. |
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| 12. |
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| 13. |
- Karlsson, Anette, et al.
(författare)
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Automatic and Quantitative Assessment of Total and Regional Muscle Tissue Volume using Multi-Atlas Segmentation
- 2014
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Accurate and precise assessment of human muscle tissue is important for further understanding of different muscle diseases and syndromes. We present a rapid whole body MR method for automatic quantification of total and regional muscle volume. The method is based on multi-atlas segmentation of intensity corrected water-fat separated images. The method was validated with a leave-one-out approach, using manually segmented atlases from 10 subjects as ground truth. The result gave a coefficient of variation on total muscle volume equal to 1.25±1.35 % (mean ± standard deviation). The method enables cost-efficient large-scale studies, investigating conditions such as sarcopenia and muscular dystrophies.
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| 14. |
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| 15. |
- Läthén, Gunnar, et al.
(författare)
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Automatic Tuning of Spatially Varying Transfer Functions for Blood Vessel Visualization
- 2012
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Ingår i: IEEE Transactions on Visualization and Computer Graphics. - : IEEE. - 1077-2626 .- 1941-0506. ; 18:12, s. 2345-2354
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Tidskriftsartikel (refereegranskat)abstract
- Computed Tomography Angiography (CTA) is commonly used in clinical routine for diagnosing vascular diseases. The procedure involves the injection of a contrast agent into the blood stream to increase the contrast between the blood vessels and the surrounding tissue in the image data. CTA is often visualized with Direct Volume Rendering (DVR) where the enhanced image contrast is important for the construction of Transfer Functions (TFs). For increased efficiency, clinical routine heavily relies on preset TFs to simplify the creation of such visualizations for a physician. In practice, however, TF presets often do not yield optimal images due to variations in mixture concentration of contrast agent in the blood stream. In this paper we propose an automatic, optimization- based method that shifts TF presets to account for general deviations and local variations of the intensity of contrast enhanced blood vessels. Some of the advantages of this method are the following. It computationally automates large parts of a process that is currently performed manually. It performs the TF shift locally and can thus optimize larger portions of the image than is possible with manual interaction. The method is based on a well known vesselness descriptor in the definition of the optimization criterion. The performance of the method is illustrated by clinically relevant CT angiography datasets displaying both improved structural overviews of vessel trees and improved adaption to local variations of contrast concentration.
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| 16. |
- Moreno, Rodrigo, 1973-, et al.
(författare)
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Correlations between fabric tensors computed on cone beam and microcomputed tomography images
- 2014
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Ingår i: Computational Vision and Medical Image Processing IV. - : CRC Press. - 9781138000810 ; , s. 393-398
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Konferensbidrag (refereegranskat)abstract
- Computational Vision and Medical Image Processing. VIPIMAGE 2013contains invited lectures and full papers presented at VIPIMAGE 2013 - IV ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing (Funchal, Madeira Island, Portugal, 14-16 October 2013). International contributions from 16 countries provide a comprehensive coverage of the current state-of-the-art in the fields of: 3D Vision; Computational Bioimaging and Visualization; Computational Vision and Image Processing applied to Dental Medicine; Computational Vision; Computer Aided Diagnosis, Surgery, Therapy, and Treatment; Data Interpolation, Registration, Acquisition and Compression; Image Processing and Analysis; Image Segmentation; Imaging of Biological Flows; Medical Imaging; Physics of Medical Imaging; Shape Reconstruction; Signal Processing; Simulation and Modeling; Software Development for Image Processing and Analysis; Telemedicine Systems and their Applications; Trabecular Bone Characterization; Tracking and Analysis of Movement; Virtual Reality.Related techniques covered in this book include the level set method, finite element method, modal analyses, stochastic methods, principal and independent components analysis and distribution models. Computational Vision and Medical Image Processing. VIPIMAGE 2013 is useful to academics, researchers and professionals in Biomechanics, Biomedical Engineering, Computational Vision (image processing and analysis), Computer Sciences, Computational Mechanics and Medicine.
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| 17. |
- Moreno, Rodrigo, et al.
(författare)
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Soft classification of trabeculae in trabecular bone
- 2011
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Ingår i: Biomedical Imaging: From Nano to Macro, 2011. - : IEEE. - 9781424441273 ; , s. 1641-1644
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Konferensbidrag (refereegranskat)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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| 18. |
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| 19. |
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| 20. |
- Romu, Thobias, et al.
(författare)
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MANA - Multi scale adaptive normalized averaging
- 2011
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Ingår i: 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. - : IEEE conference proceedings. - 9781424441280 ; , s. 361-364
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Konferensbidrag (refereegranskat)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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