Current modules

File module	Operations	Description
MorphologyModule.py	removeSmallObjects	“Remove small items from the image. This is typically done for reducing small pixilar noise, dust, etc”
	fillSmallHoles	“Fill in small/medium sized “”holes”” in images. For example, lumen spaces in tubules often are detected as background and removed from the final mask. This module will fill them in.”
LightDarkModule.py	getIntensityThresholdOtsu	Thresholds the image based on dynamic Otsu threshold
	getIntensityThresholdPercent	Thresholds the image based on user supplied values. This is good for detecting where the issue is on the slide (non-white) and where folded tissue may be (very dark)
HistogramModule.py	getHistogram	Makes a histogram image in rgb space
	compareToTemplates	Compares the image’s histogram to template images provided by the user
DeconvolutionModule.py	seperateStains	Performs stain deconvolution using skimage’s built in matricies
ClassificationModule.py	pixelWise	Applies an RGB based classifier to the image whose values come from a user inputed TSV
	byExampleWithFeatures	“Computes features of template images provided by the user which have associated binary masks indicating positive and negative classes. Trained classifier is then used on images. Excellent for, e.g., pen detection (with texture) , cracks, etc”
BubbleRegionByRegion.py	roiWise	Detect contours of lines of airbubbles on slide. Contains exemplar of how to use HistoQC to iteratively loop over very large images at high mag. (work in progress)
BrightContrastModule.py	getBrightnessGray	“Computes the average value of the image in gray colorspace, which ultimately represents how bright the image is perceived”
	getBrightnessByChannelinColorSpace	Computes a triplet (one per color channel) in the desired color space. Useful for detecting outliers
	getContrast	Computes both RMS and Michelson contrast metrics
PenMarkingModule.py	identifyPenMarking	“Identities pen markings on a pixel by pixel basis by using user supplied tsv file of color values. This is usually suitable when the marking is very different from the staining (e.g., green/blue marker on pink tissue). DEPRECATED - Use ClassificationModule PixelWise”
BlurDetectionModule.py	identifyBlurryRegions	Uses a laplace matrix to determine which regions in the image are likely blurry
BasicModule.py	getBasicStats	Pulls out metadata from image header
	getMag	Pulls out base magnification. This is required by HistoQC. In the future we’ll add ability to predict magnification
	finalComputations	Computes the final number of pixels available in the output image. Too high or low of a number often indicate incorrect processing or image outliers
	finalProcessingSpur	“Removes spurious morphology from the final mask. Essentially small “”arms”” of tissue are rounded off and removed”
	finalProcessingArea	“Removes larger islands from the output mask, e.g., isolated pieces of tissue”
SaveModule.py	saveFinalMask	Saves both the output mask from HistoQC but also the overlay on the original image
	saveThumbnails	Save thumbnails for easier viewing. This needs to be completed for the UI to work
AnnotationModule.py	xmlMask	Loads an Aperio XML file to mask out regions of the image, limitating artifact detection and metric computation to solely regions of interest
LocalTextureEstimationModule.py	estimateGreyComatrixFeatures	Estimate grey comatrix features for background or final images