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Table 4: Structural parameters, calculated on the interpolated micro-CT image and the optical image, for sample Ti-B
2006
Table 1. Cardiac Function Estimation in Mice (n = 5) Using the Micro-CT
Table 1: Characteristics of the two micro-CT devices.
2006
Table 1 Porosity values as obtained via pycnometer method and micro CT scanning
2005
"... In PAGE 5: ....2. Scaffold analysis per design were fabricated and passed over for MicroCT scanning. Table1 shows the porosity values as calculated from the two 3D modeling programs. Fig.... ..."
Table 1. Comparison of contour features for two sets of images - letters and blood vessels.
"... In PAGE 5: ... The ICA components come from a different word quot;zero quot;. Table1 results are shown, that demonstrate inner-class compactness and outer-class separability of different features. This veri es the usefulness of ICA in the post-processing stage of Complex Contour Fourier features.... ..."
Table 1: Centroids of detected blood vessels Block no. Diameter
"... In PAGE 9: ....1.2 Blood Vessel Centroid A time sequence of infrared emission frames may be viewed as a three-dimensional image, and moments can be applied to calculate the blood vessel centroid detected by the block detection algorithm. Table1 lists the centroid coordinates of the rst 15 blocks calculated using Eq.(3).... ..."
Table 1. Statics for curving the blood vessel meshes.
2004
"... In PAGE 15: ... is computed as the maximum distance between sample points of each mesh entity classified on curved model boundary and their corresponding closest points on the boundary. Statics for curving the blood vessel meshes are presented in Table1 . The results clearly demonstrate that geometric approximation error in terms of normalized maximum distance devia- tion has been improved by increasing the geometric approximation order especially in going from linear to cubic geometry.... In PAGE 15: ...oundary. Statics for curving the blood vessel meshes are presented in Table 1. The results clearly demonstrate that geometric approximation error in terms of normalized maximum distance devia- tion has been improved by increasing the geometric approximation order especially in going from linear to cubic geometry. Table1 also includes the statics on which local mesh modifications were used to correct the invalid elements. The shape manipulation operations are the most frequently applied in this exam- ple.... ..."
Cited by 1
Table 3: Contents of CT Image Database
1994
"... In PAGE 8: ...We applied CANDID to this problem of retrieving pulmonary CT imagery from a database containing a total of 220 lung images taken from pulmonary CT studies of 34 di erent patients (see Table3 ). Each image was 512 512 pixels in size, consisting of 12-bit grayscale data.... ..."
Cited by 8
Table 3: The six CT slices in the table were retrieved from our pulmonary CT database using six di erent distance/similarity measures. Each column shows the retrieval rankings of the individual images resulting from a search based on a given measure. Those measures that retrieved images with high rankings (1 being the highest), are judged to be superior to those that ranked the images relatively lower.
1995
Cited by 75
Table 2: Contents of CT Image Database We applied CANDID to this problem of retrieving pulmonary CT imagery from a database containing a total of 220 lung images taken from pulmonary CT studies of 34 di erent patients (see Table 2). Each image was 512 512 pixels in size, consisting of 12-bit grayscale data. For this application, we are primarily interested in retrieving images containing similar textures. We have previously demonstrated that four Laws texture energy measures13,14 can be used to discriminate between images of lungs that are a ected by by di erent diseases.11,12 A drawback to using Laws texture energy measures is the amount of time it takes to generate features that can be submitted to a clustering algorithm for signature generation. We have recently been experimenting with other features that contain local texture information. One of these feature sets, which we simply call \local statistics quot;, is very fast to compute as compared to the Laws texture energy measures. Before we computed any statistical features, we rst isolated those areas of each CT image that were rep- resentative of lung tissue,15 and we worked only within these areas. The local statistics data set consisted of three features computed on the grayscale values surrounding each lung pixel: standard deviation ( ), skewness, and kurtosis. The pixel intensity values within a circular region of diameter 9 around each pixel were extracted (surrounding pixels falling outside of the lung were ignored). Three statistical features were computed at each
1995
Cited by 75
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