A WSI Image processing application
Python Package: PyHistopathology
Read our documentation at https://pyhistopathology.readthedocs.io/en/latest/
Command line tool:
python3 WSI_PATCH_Extraction.py -args Mandatory args
- -i: input svs file path
- -o: output folder path
- -f: input folder for path
- Note: you should use either -i or -f, cannot use both. Additional args
- -c: criteria
- criteria: Random or None, Default is None
- -s: patch size
- Size of the patch to extract, default is (256,256)
- -n: number of patches
- Only should be given for -c Random. Default value is 2000.
- -a: input xml
- if annotations are provided annotations file path should be given. Otherwise don't use this arg.
use WSI_Scanning.readWSI() to read an WSI Image
Input: WSI path or directory
Output: functioning numpy array of WSI Image with dtype int32 Function
readWSI(WSI_path, magnification_level, annotation_file, annonated_level) Arguments
- WSI_path: Directory of WSI
- magnification_level: level of zoom, example (40x,20x,10x,5x). Default magnification is “20x” - Note if magnification 40x for max zoom level of 20x image an error will be raised.
- annotation_file: Default annotation = None. if annotation are available in xml formats. use annotation = inputxml file path.
- annonated_level= if annotation is not giving no need to consider this variable. if annotation is given then mention z-axis of annotations. Default annotatedlevel =0
###Reading image example from WSI_Preprocessing.Preprocessing import WSI_Scanning import cv2 img,slide_dim = WSI_Scanning.readWSI("example.svs") cv2.imwrite("example.png",img)
use Denoising.denoising() to remove stains, folds and other background noise in WSI
- input: WSI Path or directory
- Output: functioning numpy array of WSI Image (After denoising) with dtype int32.
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, example (40x,20x,10x,5x). Default magnification is “20x”
- Note if magnification 40x for max zoom level of 20x image an error will be raised.
- filtering: GuassianBlur, RGBThersholding, 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, green-value, blue_values are threshold for RGB
- 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
- 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
import cv2 # Here mandatory options are example.svs and magniﬁcation img = Denoising.denoising("example.svs", "20x" ) cv2.imwrite("example.png",img)
use Extractingpatches.extractingPatches() to extract patches from WSI.
- input: WSI Path or directory
- output: patches from WSI.
Function: extractingPatches(inputsvs, outputpath, magnification, patch_extraction_creatia, number_of_patches, filtering, patch_size, upperlimit, lowerlimit, red_value, green_value, blue_value, Annotation, Annotationlevel, Requiredlevel, reconstructionimagepath)
Arguments - inputsvs, magnification, patch_extraction_creatia, filtering, patch_size, upperlimit, lowerlimit, red_value, green_value, blue_value, Annotation, Annotationlevel, Requiredlevel, arguments is same as denosing module. - patch_extraction_creatia: random, None - - Default is None. For extracting a fixed number of patches for WSI we can use random. - Default number of patches is 2000 - outputpath: folder to store the extracted patches - reconstructionimagepath: we you want to compare the patches with WSI we can mention the reconstructionimagepath. - Default is None - - Note: it only works with patch_extraction_creatia = None.
- Note: For WSI number of patches can exceed 20k.
from WSI_Preprocessing.Preprocessing import Extarctingpatches import cv2 img = Extarctingpatches.extractingPatches("example.svs","temp" ,"20x" ) cv2.imwrite("exampler.png",img) # Here mandatory options are example.svs and magnification, and outputpath Extractingpatches.extractingPatches(example.svs, outputpath, magnification)
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