Course Description:

The course offers an introduction to programming with python and explains basic algorithms for processing microscopic images. In this hands-on course, the students with biology/biochemistry background learn to implement conventional image segmentation algorithms (discontinuity-based methods and similarity-based methods) as well as state-of-the-art machine learning methods (supervised learning, feedforward neural networks, convolutional neural networks) on calcium imaging data.

The student will be able to independently work with the functions provided by the following python packages:

• OpenCV Python

• PIL: Python Imaging Library and its updated version, Pillow. (Note: Pillow and PIL cannot co-exist in an environment. Before installing Pillow, please uninstall PIL. )

• Matplotlib

• NumPy

• SciPy

• Scikit-image

• Fast.ai

References:

• Gonzalez, Rafael C., Richard Eugene Woods, and Steven L. Eddins. Digital Image Processing (4th Edi6on). Pearson, 2017.

• Matthes, Eric. Python crash course: a hands-on, project-based introduction to programming. No Starch Press, 2015.

• Introduction to OpenCV-Python Tutorials

Course materials: can be found here.