Lesson 7
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CLAHE
Contrast Limited Adaptative Histogram Equalization (CLAHE)
Each time an image is acquired, window and level parameters must be adjusted to maximize contrast and structure visibility. This must be done before the image is saved in any other format than the generic format of the acquisition software HIS.For the moment, very little post-processing in addition to window-level is applied to the image after its acquisition. This is due in part to the good quality of the image without processing, but also because of the short experience and tools we have working with 16 bit images.
CLAHE seems a good algorithm to obtain a good looking image directly from a raw HIS image, without window and level adjustment. This is one possibility to automatically display an image without user intervention. Further investigation of this approach is necessary.
CLAHE
CLAHE was originally developed for medical imaging and has proven to be successful for enhancement of low-contrast images such as portal films.
The CLAHE algorithm partitions the images into contextual regions and applies the histogram equalization to each one. This evens out the distribution of used grey values and thus makes hidden features of the image more visible. The full grey spectrum is used to express the image.
Contrast Limited Adaptive Histogram Equalization, CLAHE, is an improved version of AHE, or Adaptive Histogram Equalization. Both overcome the limitations of standard histogram equalization.
A variety of adaptive contrast-limited histogram equalization techniques (CLAHE) are provided. Sharp field edges can be maintained by selective enhancement within the field boundaries. Selective enhancement is accomplished by first detecting the field edge in a portal image and then only processing those regions of the image that lie inside the field edge. Noise can be reduced while maintaining the high spatial frequency content of the image by applying a combination of CLAHE, median filtration and edge sharpening. This technique known as Sequential processing can be recorded into a user macro for repeat application at any time. A variation of the contrast limited technique called adaptive histogram clip (AHC) can also be applied. AHC automatically adjusts clipping level and moderates over enhancement of background regions of portal images.
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2D Minimum Filter
The minimum filter replaces the value of a pixel by the smallest value of neighboring pixels covered by a NxN matrix mask. The size of the mask can be adjusted via the input field kernel size. A value of 3 denotes a 3x3 mask.
If applied to a binary label field the minimum filter implements a so-called erosion operation. It reduces the size of a segmented region by removing pixels from its boundary.
2D Median Filter
The median filter is a simple edge-preserving smoothing filter. It may be applied prior to segmentation in order to reduce the amount of noise in a stack of 2D images.
The filter works by sorting pixels covered by a NxN mask according to their grey value. The center pixel is then replaced by the median of these pixels, i.e., the middle entry of the sorted list.
The size of the pixel mask may be adjusted via the text field labeled kernel size. A value of 3 denotes a 3x3 mask. An odd value is required.
2D Maximum Filter
The maximum filter replaces the value of a pixel by the largest value of neighboring pixels covered by a NxN mask. The size of the mask can be adjusted via the input field kernel size. A value of 3 denotes a 3x3 mask.
If applied to a binary label field the maximum filter implements a so-called dilation operation. It enlarges the size of a segmented region by adding pixels to its boundary.
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