Course Identification

Practical Image Analysis for Biology

Lecturers and Teaching Assistants

Ms. Ofra Golani

Course Schedule and Location

Second Semester
9-10 May, 13-17 May, 22-24 May, 9:00-13:00 FGS room B,

Field of Study, Course Type and Credit Points

Life Sciences: Laboratory; Elective; 1.00 points
Life Sciences (Systems Biology Track): Laboratory; 1.00 points
Life Sciences (Molecular and Cellular Neuroscience Track): Laboratory; 1.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Laboratory; 1.00 points


The course is in a condensed format: A two-week course, everyday from 9-13. Students must plan their time ahead to ensure that you can attend all lectures.  It will consist of both theoretical and practical classes with demos and hands-on analysis of image examples.

Course schedule breakdown (three weeks because of SHAVUT):
Week 1: Wednesday-Thursday (9-10 May) 9:00-13:00
Week 2: Sunday-Thursday (13-17 May) 9:00-13:00
Week 3: Tuesday-Thursday (22-24 May) 9:00-13:00


Although there are no prerequisites, but we recommend that students will take the course when they already have some experience with imaging.



Language of Instruction


Registration by


Attendance and participation


Grade Type

Pass / Fail

Grade Breakdown (in %)

Homework assignments. Completion of the assignments and attendance of all lectures is mandatory.

Evaluation Type

Final assignment

Scheduled date 1


Estimated Weekly Independent Workload (in hours)



Bioimaging is a central set of tools in Biological research. It is interdisciplinary science which requires understanding of biology, microscopy, fluorescence probes, image processing and image analysis. Tremendous volumes of multi-dimensional biological data are now being generated in almost every branch of biology. How to visualize and how to interpret such images in quantitative, objective and efficient way is essential knowledge for students who use bioimaging for their research.

The course will cover useful techniques and tools used for analysis of images in Biology. Topics that will be addressed include: basic concepts of digital images, visualization of multi-dimensional data, image processing in preparation for analysis, noise and image enhancement, binary operations, object / particle analysis and intensity and morphometric measurements, tracking for analysis of particles or cells movement, filament analysis, colocalization and deconvolution.

We will address the issues of image ethics and proper image acquisition for image analysis. We will introduce the students with two image analysis software: the free ImageJ/Fiji package and the commercial Imaris software. We will focus on examples and hands-on exercises that will teach a broad functionality of these tools and the concepts needed for building suitable workflow for a given application.

A whole module is dedicated to workflow building and analysis automation using scripting with ImageJ macro language. We do not assume previous programming background. 

The course include a Bring-Your-Own-Data module, in which students present their own image analysis applications, and everyone discuss possible approaches for solutions. Finally the students apply the learned tools to their own data, with help of the course teachers, to get a head start with image analysis for their own research. 

Learning Outcomes

Upon successful completion of this course the students should be able to:
  1. Demonstrate knowledge and understanding of Image Ethics: What type of image processing is acceptable for publication and what type is non-acceptable.
  2. Demonstrate understanding of the proper image acquisition needed for image analysis.
  3. Demonstrate understanding of basic concepts of image processing and analysis as detailed in the objectives part above.
  4. Display various types of (multi-dimensional) image datasets using Fiji and Imaris software.
  5. Perform manual and automatic measurements using the Fiji software.
  6. Apply different pre-processing techniques  
  7. Use Fiji to segment and quantify objects
  8. Automate workflows in Fiji software using scripts in ImageJ macro language   
  9. Perform object segmentation and quantification and track objects using Imaris software.
  10. Perform pixel based and object based colocalization analysis.
  11. Explain why deconvolution is needed and how to apply it to their data.

Reading List