## Prerequisites

Before starting this lesson, you should be familiar with:

## Learning Objectives

After completing this lesson, learners should be able to:
• Understand shape measurements and their limitations

• Perform shape measurements on objects

## Motivation

Our eyes are extremely good in distinguishing forms and patterns and this has proven to be a powerful tool for characterizing different cell-types, functions, phenotypes, and more. In image processing, we use shape measurements (e.g. area, volume, elongation, …) for an automated and objective characterization of forms. Consequently, one can address scientific questions or filter objects that should be used for further processing. Typically, we apply shape measurements on a labeled image. The labeled image, as obtained after a connected component analysis, defines a set of objects in 2D/3D. However, for a quick manual analysis delineating objects by drawing so-called ROIs (regions of interest) is another possibility.

## Concept map

graph TD li[Object regions] --> sa("Shape Analysis") sa --> table["Object table
oid | area | perimeter | circularity
001 | 222 | 56 | 0.9
002 | 500 | 101 | 0.2 "] style table text-align:left

## Activities

#### Measure object shapes in a digital image

• Using a drawing board discuss some shape features and concepts, e.g.,
• Area
• Perimeter: Like all surface measurements, this is tricky, e.g.,
• Circularity = ( 4 * Pi * Area ) / Perimeter^2: Designed to be 1.0 for a perfect circle
• Aspect ratio: Major ellipse axis length / Minor ellipse axis length
• Solidity = Convexity = Area / Convex_hull_area: Useful to find objects with spikes or indentations
• Ellipse fit parameters or Elongation: Useful to measure object elongation
• Discuss issues with small object in digital images, e.g., by exploring a square (=circle?!) of 2x2 pixels.
• Open an image with objects, e.g.,
• Perform shape features such as area, perimeter, circularity, and solidity
• Discuss how objects can be distiguished by various shape measurements

Show activity for:

## ImageJ GUI ROI

• Open an image with objects of different shapes (see activity preface)
• Use `Analyse > Tools > ROI Manager` to open the ROI manager
• Use the ROI tools, e.g. the Polygon selection in the Fiji menu bar to delineate some objects in the image
• Use `ROI Manager > Add` to record each ROI
• Use `ROI Manager > Rename...` to give them meaningful names
• For easier region identification use `ROI Manager > More > Options`
• Use ROI names as labels
• Display all regions using `ROI Manager`
• Show All
• Labels
• Once all are added use `ROI Manager > More > Save...` to store the ROIs
• This is important to document your work; the ROI file can be opened via drag&drop on Fiji
• Use `Analyse > Set Measurements` to configure what to measure:
• Area
• Centroid
• Perimeter
• Fit Ellipse
• Feret’s Diameter
• Shape Descriptors
• Display label (adds a column with the object name)
• Use `ROI Manager > More > Multi Measure` to measure all ROIs at once
• Use `Image > Properties` to check the image calibration
• Use `Results > File > Rename` to indicate the image calibration in the table name
• Use `Image > Properties` to change the image to pixel units
• Measure all ROIs again and change the table name to indicate the calibration
• Understand all the measurements
• Compare calibrated and pixel unit measurements
• Go through the columns and see which object has an extreme value and why
• See ImageJ measurements documentation
• Select one object and use `Edit > Selection > Convex Hull` to see the convex hull

## ImageJ GUI MorphoLibJ

• Open image xy_8bit_labels__four_objects.tif
• Perform shape measurements and discuss their meanings [Plugins > MorphoLibJ > Analyze > Analyze Regions]
• Explore results visualisation [Plugins > MorphoLibJ > Label Images > Assign Measure to Label]
• Add a calibration of 2 micrometer to the image and check which shape measurements are affected.
• Perform a shape analysis for 3D image xyz_16bit_labels__spindle_spots.tif and [Plugins > MorphoLibJ > Analyze > Analyze Regions 3D]

## skimage napari

#### Practice measuring object shapes in an image

Practice performing shape measurements.

Show activity for:

## ImageJ GUI

Open image xy_16bit_labels__nuclei.tif Using MorpholibJ:

1. Measure object shapes and find the label index of the nucleus with the largest perimeter
2. Change the pixel size to 0.5 um and repeat the measurements. Why do some parameters change while others don’t?
3. (Optional) Create an image where each object is coloured according to the measured circularity

## Solution

1. [Plugins > MorphoLibJ > Analyze > Analyze Regions] the upper right nuclei.
2. Some features are the ratio of dimensional features and so are independent of the spatial calibration.
3. [Plugins > MorphoLibJ > Label Regions > Assign Measure to Label].

## Assessment

### True or false? Discuss with your neighbour

• Circularity is independent of image calibration.
• Area is independent of image calibration.
• Perimeter can strongly depend on spatial sampling.
• Volume can strongly depend on spatial sampling.
• Drawing test images to check how certain shape parameters behave is a good idea.

## Solution

• Circularity is independent of image calibration True
• Area is independent of image calibration. False
• Perimeter can strongly depend on spatial sampling. True
• Volume can strongly depend on spatial sampling. True
• Drawing test images to check how certain shape parameters behave is a good idea. True

## Follow-up material

Recommended follow-up modules: