Vocabulary

This page defines the terms used in the course.

Images

Term	Meaning
Pixel	One element of the image. It holds a number, not a colour.
Intensity image	An image whose pixel values are measured brightness, as it comes off the microscope.
Bit depth	How large a pixel value may be. An 8-bit image holds 0 to 255, a 16-bit image 0 to 65535.
Calibration	The physical size of a pixel. Without it, every area and length is in pixels rather than microns.
Channel	One image of a set acquired of the same field through different filters, for example DAPI and GFP.
LUT (lookup table)	The mapping from pixel value to displayed colour. Changing it changes the picture, never the data. Called a colormap in matplotlib.
Histogram	A count of how many pixels hold each value.
Saturation	Pixels pinned at the maximum value. Their true brightness has been lost and cannot be recovered.
Registration	Finding the transform that brings one image into alignment with another.

Segmentation

Term	Meaning
Segmentation	Deciding which pixels belong to the objects of interest.
Threshold	A value separating foreground from background. Applying one produces a binary image.
Otsu’s method	A rule for choosing a threshold automatically from the histogram.
Foreground / background	The pixels belonging to objects, and everything else.
Binary image	An image with only two values, foreground and background. What a threshold produces.
Mask	A binary image used to select pixels from another image. The same array as a binary image, in a different role.
Label image	An image whose pixel values are object numbers: 1 for the first object, 2 for the second, and so on. Its maximum value is the number of objects.
Semantic segmentation	Answers what kind of thing is this pixel? Every pixel gets a class, and nothing distinguishes one object from another.
Instance segmentation	Answers which object is this pixel part of? Required for anything measured per object.
Connected component labeling	Turning a binary image into a label image by giving every connected group of foreground pixels its own number. Cannot separate objects that touch.
Morphological operations	Operations on the shape of a binary image: erosion, dilation, opening, closing, hole filling.
Distance transform	An image in which each foreground pixel holds its distance to the nearest background pixel.
Watershed	Splitting touching objects by treating the distance transform as a landscape and flooding it from seed points.
Seed	A starting point inside an object, from which the watershed grows that object.

Processing

Term	Meaning
Filter	An operation replacing each pixel by a function of its neighbourhood, such as a Gaussian blur or a median filter.
Background subtraction	Removing a slowly varying offset so that a single threshold works across the whole field.
Uneven illumination	Brightness varying across the field because of the optics rather than the sample. Multiplicative, so corrected by division.
Denoising	Reducing noise before segmentation. A Gaussian filter suits general graininess, a median filter suits isolated extreme pixels.

Machine learning

Term	Meaning
Pixel classification	Training a model to label each pixel by class, using features computed around it. What Trainable Weka does.
Feature (for a classifier)	One measured property of a pixel and its surroundings, such as local blur or edge strength. Not the same as a feature measured per object.
Probability map	An image holding, for each pixel, the classifier’s confidence that it belongs to a class.
Classifier	The trained model itself, saved as a .model file and reusable on new images.
Shallow learning	You choose the features, the model learns how to weigh them.
Deep learning	The model learns the features from data as well, which is why it needs a great deal of it.
Cellpose	A pretrained deep model that outputs instance segmentations directly.

Measuring

Term	Meaning
Feature (of an object)	One measured property of a segmented object.
Morphology feature	A feature computed from shape alone: area, perimeter, eccentricity, solidity, extent.
Intensity feature	A feature summarising pixel values inside an object, which needs a second image to measure.
Region properties	The standard set of per-object measurements. regionprops in scikit-image, Analyze Particles in Fiji.
Effect size	The difference between two groups expressed in standard deviations, saying how large a difference is rather than only whether it is detectable.

Validation

Term	Meaning
Ground truth	A reference segmentation, usually drawn by an expert. A reference, not necessarily the truth.
IoU (intersection over union)	Shared pixels divided by pixels covered by either. Also called the Jaccard index.
Dice coefficient	Twice the shared pixels divided by the total of both. Always reads higher than IoU for an imperfect overlap.
True positive, false positive, false negative	An object correctly found, invented, or missed.
Precision	Of the objects you found, the fraction that are real.
Recall	Of the real objects, the fraction you found.
F1 score	The balance of precision and recall, in one number.
Matching threshold	How much overlap is required before a predicted object counts as matching a real one. Conventionally an IoU of 0.5, and worth reporting.

Fitting

Term	Meaning
Model	A function with adjustable parameters, chosen to describe the process being measured.
SSE (sum of squared errors)	The usual measure of how badly a set of parameters describes the data.
Residual	The difference between a data point and the fitted curve. Structure in the residuals means the model is the wrong shape.
IC50	The concentration at which an effect is reduced by half.

Easy to confuse

Binary image and label image. A binary image says whether a pixel is foreground (True) or background (False). A label image says which object it belongs to (e.g. 1, 2, 3 for object IDs 1, 2, 3). Where there’s no object at all, the label image has a value of 0.

Binary image: “Semantic segmentation” (what category is this pixel?) Label image: “Instance segmentation” (which object is this pixel part of?)

Feature In pixel classification a feature is a property of a pixel, computed from its neighbourhood. In measurement a feature is a property of an object. Both are standard, and the context makes clear which is meant.