Nonrigid registration of medical images is an important procedure in many aspects of current biomedical and bioengineering research. For example, it is a necessary step for studying the variation of biological tissue ...
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ISBN:
(纸本)0819444294
Nonrigid registration of medical images is an important procedure in many aspects of current biomedical and bioengineering research. For example, it is a necessary step for studying the variation of biological tissue properties, such as shape or diffusion properties across population, compute population averages, or atlas-based segmentation. Recently we have introduced the Adaptive Bases registration algorithm as a general method for performing nonrigid registration of medical images and we have shown it to be faster and more accurate than existing algorithms of the same class [1-3]. The overall properties of the Adaptive Bases algorithm are reviewed here and the method is validated on applications that include the computation of average images, atlasbasedsegmentation, and motion correction of video images. Results show the Adaptive Bases algorithm to be capable of producing high quality nonrigid matches for the applications above mentioned.
Volumetric analysis of the brain from MR images is an important biomedical research tool. segmentation of the brain parenchyma and its constituent tissue types, the gray matter and the white matter, is necessary for v...
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ISBN:
(纸本)0819440086
Volumetric analysis of the brain from MR images is an important biomedical research tool. segmentation of the brain parenchyma and its constituent tissue types, the gray matter and the white matter, is necessary for volumetric information in longitudinal and cross-sectional studies. We have implemented and compared two different classes of algorithms for segmentation of the brain parenchyma. In the first algorithm a combination of automatic thresholding and 3-D mathematical morphology was used to segment the brain while in the second algorithm an optical flow-based 3-D non-rigid registration approach was used to warp an MR head atlas to the subject brain. For tissue classification within the brain area a 3-D Markov Random Field model was used in conjunction with supervised and unsupervised classification. The algorithms described above were validated on a data set provided at the Internet Brain segmentation Repository that consists of 20 normal T1 volumes (3 nim. slice thickness) with manually segmented brain and manually classified tissues. While the morphological segmentation algorithm had an average similarity index of 0.918, the atlas-based brain segmentation algorithm has an average similarity index of 0.953. The supervised tissue classification had an average similarity index of 0.833 for gray matter voxels and 0.766 for white matter voxels. The performance of these algorithms is quite acceptable to end-users both in terms of accuracy and speed.
The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR...
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The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain, Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters, For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.
The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free form deformations can be used for the accurate segmentation of internal structures in MR...
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ISBN:
(纸本)0819427837
The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum and the head of the caudate have been segmented manually on one of the volumes and mapped back onto all the other volumes using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was repeated to test intra-rater variability. Contours were quantitatively compared using a similarity index defined as 2 times the area encircled by both contours divided by the sum of the areas encircled by each contour. This index ranges from 0 to 1 with zero indicating zero overlap and one indicating a perfect agreement between two contours. It is sensitive to both displacement and differences in shape and it is thus preferable to a simple area comparison. Results indicate that the method we propose can be used to segment accurately and fully automatically large and small structures in high resolution 3D images of the brain. The average similarity indices between the manual and automatic segmentations are 0.96, 0.97, and 0.845 for the whole head, the cerebellum, and the head of the caudate respectively. These numbers are 0.97, 0.97, and 0.88 when two manual delineations are compared. Statistical analysis reveals that the differences in mean similarity indices between the two manual delineations and between the manual delineations and the automatic segmentation method are statistically significant for the whole head and the caudate but not for the cerebellum. It is shown, however, that similarity indices in the range of 0.85 correspond to contours that are virtually undistinguishable.
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