Denoising¶
Use Denoising.denoising() to remove stains, folds and other background noise in Whole-Slide Images
Usage¶
denoising(inputsvs, magnification, filtering, patch_size, upperlimit, lowerlimit, red_value, green_value, blue_value)
Arguments¶
inputsvs¶
Path or location of WSI.
magnification¶
Level of zoom, for example 40, 20, 10, or 5. Default magnification level is 20. - Note: if magnification 40x for max zoom level of 20x image an error will be raised.
filtering¶
GuassianBlur, RGBThersholding, or None
GuassianBlur: Homogeneity calculations based on image smoothing and Gaussian blur equations. We compute sum of square differences between two consecutive Gaussian blurred images as score for homogeneity.
- Upper limit: Upper threshold of homogeneity score. Default value is 9500 with kernel size of 1111
- Lower limit: lower threshold of homogeneity score. default value is 1500 with kernel size of 1111
- Patch size: Not significant parameters for GuassianBlur filtering
RGBThersholding: Validated patches based on RGB values of patches
- red_value: Red threshold
- green-value: Green threshold
- blue_value: Blue Threshold
None: Only removes Background
Note that our default is GuassianBlur technique. GuassianBlur is highly effective and requires more computational power (RAM). RGBThersholding is less effective which needs less computational power
Return Type¶
Numpy array of WSI Image (After denoising) with dtype int32