differences for PR #4

10-multi-dimensional-data.md

@@ -0,0 +1,33 @@
+---
+title: Multi-dimensional data
+teaching: 60
+exercises: 60
+---
+
+::::::::::::::::::::::::::::::::::::::: objectives
+
+- Learn about multi-dimensional image data such as 3D volumetric stacks and time-lapses.
+- Visualize multi-dimensional data interactively using Napari.
+- Learn to use the basic functionality of the Napari GUI including overlaying masks over images.
+- Construct an analysis workflow to measure properties of 3D objects in a 3D volumetric image stack.
+- Construct an analysis workflow to measure changes over time from a time-lapse movie.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: questions
+
+- How can we scikit-image to perform image processing tasks on multi-dimensional image data?
+- How can we visualise the results using Napari?
+
+::::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::: keypoints
+
+- We open access the Napari n-dimensional image viewer with `Napari.Viewer` objects.
+- `Image` and `Label` layers can be added to a viewer with `Napari.Viewer.add_image`
+  and `Napari.Viewer.add_label` respectively.
+- Many scikit-image functions such as `skimage.filters.gaussian`, `ski.threshold.threshold_otsu`,
+  `ski.measure.label` and `ski.measure.regionprops` work with 3D image data.
+- Iterate through time-points to analyse time-lapse movies.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::

config.yaml

@@ -58,7 +58,7 @@ contact: '[email protected]'
 # - another-learner.md
 
 # Order of episodes in your lesson
-episodes: 
+episodes:
 - 01-introduction.md
 - 02-image-basics.md
 - 03-skimage-images.md
@@ -68,15 +68,16 @@ episodes:
 - 07-thresholding.md
 - 08-connected-components.md
 - 09-challenges.md
+- 10-multi-dimensional-data.md
 
 # Information for Learners
-learners: 
+learners:
 
 # Information for Instructors
-instructors: 
+instructors:
 
 # Learner Profiles
-profiles: 
+profiles:
 
 # Customisation ---------------------------------------------
 #

md5sum.txt

@@ -1,7 +1,7 @@
 "file" "checksum" "built" "date"
 "CODE_OF_CONDUCT.md" "c93c83c630db2fe2462240bf72552548" "site/built/CODE_OF_CONDUCT.md" "2023-12-18"
 "LICENSE.md" "b24ebbb41b14ca25cf6b8216dda83e5f" "site/built/LICENSE.md" "2023-12-18"
-"config.yaml" "101b3ac4b679126bb1f437306eb1b836" "site/built/config.yaml" "2023-12-18"
+"config.yaml" "78121f0e6cb02000adb1d5a5962b2c61" "site/built/config.yaml" "2024-01-03"
 "index.md" "6e80c662708984307918adfad711e15f" "site/built/index.md" "2023-12-18"
 "episodes/01-introduction.md" "9755639c515fdbf752422e2e59128f63" "site/built/01-introduction.md" "2023-12-18"
 "episodes/02-image-basics.md" "6e18d2c930b9dbee94c6efef3c9a5622" "site/built/02-image-basics.md" "2024-01-03"
@@ -12,6 +12,7 @@
 "episodes/07-thresholding.md" "bc0b3a64255ef9dd359d4cf3188aba83" "site/built/07-thresholding.md" "2023-12-18"
 "episodes/08-connected-components.md" "c4b727de6f6f5ea4f8ef5467759c85bd" "site/built/08-connected-components.md" "2024-01-03"
 "episodes/09-challenges.md" "655bdca8cab5d28dc6b2c2e9275aaecc" "site/built/09-challenges.md" "2023-12-18"
+"episodes/10-multi-dimensional-data.md" "09f55c6449a5e4a41c5a6d0d9817c4a0" "site/built/10-multi-dimensional-data.md" "2024-01-03"
 "instructors/instructor-notes.md" "b1c166a544eb4b9b91f3ac8f2dafd549" "site/built/instructor-notes.md" "2023-12-18"
 "learners/discuss.md" "ad762c335f99400dc2cd1a8aad36bdbd" "site/built/discuss.md" "2023-12-18"
 "learners/edge-detection.md" "fdbcee7436e104e6587e1cb40cd37d18" "site/built/edge-detection.md" "2023-12-18"