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Detecting microvascular changes in the mouse spleen using optical computed tomography.

McErlean CM, Boult JK, Collins DJ, Leach MO, Robinson SP, Doran SJ - Microvasc. Res. (2015)

Bottom Line: A significant difference in total splenic volume was found between vehicle and ZD6126-treated cohorts, with mean volumes of 61±3mm(3) and 44±3mm(3) respectively (both n=3, p=0.05).Textural statistics for each sample were calculated using grey-level co-occurrence matrices (GLCMs).Standard 2-D GLCM analysis was found to be slice-dependent while 3-D GLCM contrast and homogeneity analysis resulted in separation of the vehicle and ZD6126-treated cohorts over a range of length scales.

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research, Sutton SM2 5NG, UK. Electronic address: cmcerlean@icr.ac.uk.

No MeSH data available.


Related in: MedlinePlus

a. Example 2-D slices from each sample region-of-interest after histogram equalisation and median filtering. ZD6126-treated spleens (T1–3) demonstrate visibly sharper boundaries between regions than vehicle spleens (V1–3). b. 3-D contrast, and c. 3-D homogeneity for each sample, for different pixel displacements (average of four angles 0°, 45°, 90° and 135°). The solid lines show the mean values for each group at each pixel displacement and there is a clear separation between the groups for a range of pixel displacements. This gives an indication of the length scales of the features that are different between the groups.
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f0030: a. Example 2-D slices from each sample region-of-interest after histogram equalisation and median filtering. ZD6126-treated spleens (T1–3) demonstrate visibly sharper boundaries between regions than vehicle spleens (V1–3). b. 3-D contrast, and c. 3-D homogeneity for each sample, for different pixel displacements (average of four angles 0°, 45°, 90° and 135°). The solid lines show the mean values for each group at each pixel displacement and there is a clear separation between the groups for a range of pixel displacements. This gives an indication of the length scales of the features that are different between the groups.

Mentions: Our initial 2-D GLCM analysis, calculated as specified by (Haralick and Shanmugam, 1973), was found to be slice-dependent (Fig. 4). However, the modified GLCM analysis incorporating three reference points (Eq. (2)) reduced the influence of analysis position on results. Calculation of the 3-D contrast and homogeneity texture parameters defined in Eqs. (3) and (4) showed a clear separation between ZD6126-treated and vehicle groups as shown in Fig. 5b. and c. The position of peak contrast was shifted to smaller pixel displacements for the ZD6126-treated samples with a peak contrast occurring at a distance of 18.2 ± 0.9 pixels for ZD6126-treated samples and 20.2 ± 0.9 pixels for the vehicle cohort, representing a physical difference of 20.8 μm.


Detecting microvascular changes in the mouse spleen using optical computed tomography.

McErlean CM, Boult JK, Collins DJ, Leach MO, Robinson SP, Doran SJ - Microvasc. Res. (2015)

a. Example 2-D slices from each sample region-of-interest after histogram equalisation and median filtering. ZD6126-treated spleens (T1–3) demonstrate visibly sharper boundaries between regions than vehicle spleens (V1–3). b. 3-D contrast, and c. 3-D homogeneity for each sample, for different pixel displacements (average of four angles 0°, 45°, 90° and 135°). The solid lines show the mean values for each group at each pixel displacement and there is a clear separation between the groups for a range of pixel displacements. This gives an indication of the length scales of the features that are different between the groups.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4542549&req=5

f0030: a. Example 2-D slices from each sample region-of-interest after histogram equalisation and median filtering. ZD6126-treated spleens (T1–3) demonstrate visibly sharper boundaries between regions than vehicle spleens (V1–3). b. 3-D contrast, and c. 3-D homogeneity for each sample, for different pixel displacements (average of four angles 0°, 45°, 90° and 135°). The solid lines show the mean values for each group at each pixel displacement and there is a clear separation between the groups for a range of pixel displacements. This gives an indication of the length scales of the features that are different between the groups.
Mentions: Our initial 2-D GLCM analysis, calculated as specified by (Haralick and Shanmugam, 1973), was found to be slice-dependent (Fig. 4). However, the modified GLCM analysis incorporating three reference points (Eq. (2)) reduced the influence of analysis position on results. Calculation of the 3-D contrast and homogeneity texture parameters defined in Eqs. (3) and (4) showed a clear separation between ZD6126-treated and vehicle groups as shown in Fig. 5b. and c. The position of peak contrast was shifted to smaller pixel displacements for the ZD6126-treated samples with a peak contrast occurring at a distance of 18.2 ± 0.9 pixels for ZD6126-treated samples and 20.2 ± 0.9 pixels for the vehicle cohort, representing a physical difference of 20.8 μm.

Bottom Line: A significant difference in total splenic volume was found between vehicle and ZD6126-treated cohorts, with mean volumes of 61±3mm(3) and 44±3mm(3) respectively (both n=3, p=0.05).Textural statistics for each sample were calculated using grey-level co-occurrence matrices (GLCMs).Standard 2-D GLCM analysis was found to be slice-dependent while 3-D GLCM contrast and homogeneity analysis resulted in separation of the vehicle and ZD6126-treated cohorts over a range of length scales.

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK Cancer Imaging Centre, The Institute of Cancer Research, Sutton SM2 5NG, UK. Electronic address: cmcerlean@icr.ac.uk.

No MeSH data available.


Related in: MedlinePlus