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Using confocal laser scanning microscopy (CLSM), bacterial cell density distribution in a 3D space near solid-liquid interface was obtained, and a robust image analysis method was developed to generate a quantitative profile of cell concentration near the interface, which allowed the precise quantification of EZ and of a transition zone (TZ), based on pixel intensity profiles.
Furthermore, most existing local methods are based on pixel intensity values, and hence are not efficient with feature vectors used in wide-baseline stereo matching.
Existing short-baseline stereo matching methods, which are mostly based on pixel intensity values, perform reasonably well and are quite fast.
MRI sets were linearly interpolated to create 3-dimensional images, and semi-automatic algorithms based on pixel intensity bands served to reconstruct the various tissues of interest.
This routine detects particles using a threshold based on pixel intensity.
Tumor margins were determined by ImageJ software from monochrome fluorescence images using automated thresholds based on pixel intensity.
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The nearest-neighbor interpolation algorithm reconstructs an image based on pixel intensities at regional maxima.
The proposed scheme has two parts: The nucleus segmentation part is based on morphological analysis, and the cytoplasm segmentation is based on pixel-intensity thresholding.
Fluorescence levels of candidate proteins and percentages of positive cells based on maximum pixel intensity were measured and fold changes compared to baseline level were calculated.
Using morphological image processing methods, nuclei were segmented based on a pixel intensity threshold, defining nuclear and cytoplasmic regions.
In our study, these were automatically defined based on the pixel intensity of the image, as explained above.
More suggestions(13)
based on pixel value
based on pixel number
based on pixel distribution
based on pixel color
based on pixel expansion
based on pixel connectivity
based on pixel energy
based on pixel classification
based on pixel readout
based on pixel count
based on pixel removal
based on image intensity
based on fluorescence intensity
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