LocallyAdaptiveContrastEnhancement

API

This class implements locally adaptive contrast enhancement for microscopy images.

Reference:

Jyh-Ying Peng, Chun-Nan Hsu, Chung-Chih Lin,
"Adaptive Image Enhancement for Fluorescence Microscopy",
Int. Conf. on Technologies and Applications of Artificial Intelligence,
pp. 9-16, 2010.

The basic idea of the algorithm is to enhance contrast by normalizing each pixel's intensity according to the standard intensity deviation in a local region around the pixel. Subsequently it should be easier to distinguish image background and relevant foreground structures from each other.

Although in principal arbitrary segmentation methods could be applied after contrast enhancement, the algorithm is optimized for subsequent binarization, e.g. by Otsu thresholding.

The algorithm does not work very well on images showing small structures on very noisy background (like P-bodies or stress granules), but is much better suited for larger structures like DAPI-stained nuclei which can more easily be distinguished from clutter, at least visually.

As an extension to the original paper this operator features a mode for component-wise application of the algorithm. This means that the image is first of all thresholded and connected components are extracted. Then the contrast enhancement is applied to each component's bounding box separately.
In the end the result image is generated from all enhanced patches after they have been thresholded, i.e. the result image in this case is already a binary segmentation of foreground and background.
This inherent binarization is done as the contrast-enhanced image contains only fractions of reasonable information and, thus, is difficult to post-process without specific knowledge only available inside of this operator.

Required input:

Optional input:

Supplemental parameters:

Output: