1986
DOI: 10.1117/12.966688
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Algorithms For Adaptive Histogram Equalization

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Cited by 6 publications
(3 citation statements)
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“…However, there was some heterogeneity in the intensity of staining between individual mitochondria which made it difficult to set a global intensity threshold without introducing error in the assignment of pixels corresponding to mitochondria, particularly those that were lightly labeled. To compensate for the different levels of labeling of individual mitochondria, the intensity distribution of light from all regions within the image was first adjusted using a locally adaptive histogram equalization algorithm (16). A single global intensity threshold could then be chosen to eliminate most of the nonmitochondrial background without significant loss of mitochondrial regions.…”
Section: Correction For Nonspecific Labelmentioning
confidence: 99%
“…However, there was some heterogeneity in the intensity of staining between individual mitochondria which made it difficult to set a global intensity threshold without introducing error in the assignment of pixels corresponding to mitochondria, particularly those that were lightly labeled. To compensate for the different levels of labeling of individual mitochondria, the intensity distribution of light from all regions within the image was first adjusted using a locally adaptive histogram equalization algorithm (16). A single global intensity threshold could then be chosen to eliminate most of the nonmitochondrial background without significant loss of mitochondrial regions.…”
Section: Correction For Nonspecific Labelmentioning
confidence: 99%
“…Histogram equalization is a widely used method in image contrast enhancement [ 44 ]. The basic idea behind this method is to redistribute all pixel values to be as close as possible to a specified desired histogram.…”
Section: Methodologiesmentioning
confidence: 99%
“…The amplitude of the interpolation artifact is dependent upon the clipping level used when calculating the histogram of the contextual region (Pizer et al 1986). If the grey scale range over a contextual region is small, then the histogram will have a sharp peak over the grey levels making up the region and will be close to zero for other values.…”
Section: Origin Of the Interpolation Artifactmentioning
confidence: 99%