Image Analysis Laboratory
Image Analysis Laboratory
Academic year 2022/2023
- Course ID
- Prof. Corrado Cali'
Prof. Stefano Gotti
- Degree course
- Cellular and Molecular Biology
- 1st year, 2nd year
- Teaching period
- Semester 2
- Course disciplinary sector (SSD)
- BIO/16 - human anatomy
- Formal authority
- Type of examination
- Practice test
- Propedeutic for
- Quantitative analysis of morphological data. Thesis.
Sommario del corso
This teaching contributes to develop a correct approach to the image analysis and quantification.
It also enables students to deepen their knowledge and abilities in the use of the computer, in the manipulation of the images and in the extraction of quantitative data.
Results of learning outcomes
- Ability to use image analysis software, image manipulation programs, and image quantification.
- Understanding the characteristics of the digital image, the principles of morphometry and statistical interpretation.
Microscopy and Digital Imaging theory
Imaging techniques: light and electron microscopy, sample preparation
Image processing, artifacts and correction filters
Introduction to ImageJ, iLastik and Blender
3D reconstructions and morphometric analysis with ImageJ and Blender
A series of theoretical lessons, followed by practical work in the laboratory. Home work to be discussed in the following lesson.
This course will thus include as many hours of traditional lessons as is possible for each student. Detailed information will be provided by each teacher based on the type of activity.
All lessons will be delivered in presence. Alternative online teaching (by streaming) may be introduced according to the University recommendations related to the status of the COVID-19 pandemic.
Learning assessment methods
The learning assessment is determined on the basis of the results of a series of laboratory and homeworks, a practical examination on the skills acquired during the laboratory and a final PowerPoint presentation of a plug-in of the Image J program that was not discussed during the laboratory.The laboratory has an obligation of attendance, equal to 75%. Students can take the final exam based on the attendance obtained. Attendance will be assessed on the basis of the exercises carried out to achieve 75% of the laboratory.The final evaluation (in thirtieths) includes the exercises carried out during the laboratory as homeworks, the practical exam and the presentation of the plug-in.
Exams will take place exclusively in presence with the only exception of students who self-declare, in relation to Covid-19, personal fragility or positivity.
Remote examinations may be introduced according to the University recommendations related to the status of the COVID-19 pandemic.
Weekly homework sets will be assigned, and their solution will be posted and (if time allows) discussed in class.
Suggested readings and bibliography
There is no specific textbook for this course. For basic and general reference, see The Image Processing Handbook, 6th edition by J.C. Russ
NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks
A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
59444/a-method-for-3d- reconstruction-virtual- reality-analysis-glial
Lessons: dal 02/03/2020 to 12/06/2020
- Enrollment opening date
- 01/02/2023 at 09:00
- Enrollment closing date
- 09/03/2023 at 23:55
- Maximum number of students
- 35 (Once this number of students is reached, enrollment will no longer be permitted!)