Complete the title above with the corresponding episode and exercise numbers.
Brief description
Measuring image features is a central goal of image analysis. Much of this course focuses on transforming raw intensity information into defined objects. Once objects are identified, one of the most fundamental skills students can learn is how to quantify their properties—particularly shape and size.
This exercise introduces the use of Scikit Image region properties (regionprops) to extract key measurements such as area, perimeter, and solidity. To support understanding, the exercise should include examples using both a binary image and an image with labeled objects. This comparison will help students grasp the underlying logic and understand when and why each approach is appropriate.
Learning objective(s)
This exercise addresses the Shape and Size Metrics learning objective.
Covers: (https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#8-measurements)
Volunteer(s)
@nicoperedo
Complete the title above with the corresponding episode and exercise numbers.
Brief description
Measuring image features is a central goal of image analysis. Much of this course focuses on transforming raw intensity information into defined objects. Once objects are identified, one of the most fundamental skills students can learn is how to quantify their properties—particularly shape and size.
This exercise introduces the use of Scikit Image region properties (regionprops) to extract key measurements such as area, perimeter, and solidity. To support understanding, the exercise should include examples using both a binary image and an image with labeled objects. This comparison will help students grasp the underlying logic and understand when and why each approach is appropriate.
Learning objective(s)
This exercise addresses the Shape and Size Metrics learning objective.
Covers: (https://github.com/carpentries-incubator/bioimage-analysis-python/wiki/Learning-Objectives#8-measurements)
Volunteer(s)
@nicoperedo