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Image Analysis Laboratory

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Image Analysis Laboratory

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Academic year 2023/2024

Course ID
SVB0054
Teachers
Corrado Cali'
Stefano Gotti
Degree course
Cellular and Molecular Biology
Year
1st year, 2nd year
Teaching period
Semester 2
Type
Elective
Credits/Recognition
4
Course disciplinary sector (SSD)
BIO/16 - human anatomy
Delivery
Formal authority
Language
English
Attendance
Obligatory
Type of examination
Practice test
Prerequisites
Applicants should be proficient in the use of PC and MS Excel. Basic knowledge of image processing, data analysis and problem-solving skills are welcomed.
Propedeutic for
Quantitative analysis of morphological data. Thesis.
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Sommario del corso

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Course objectives

This teaching contributes to developing a correct approach to image analysis and quantification. Given the plethora of digital image processing software available, priority will be given to the concepts behind image processing, analysis, and processes to be applied to the data. 

It also enables students to deepen their knowledge and abilities in the use of the computer, in the manipulation of images, and in the extraction of quantitative data.

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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

- Ability to understand the technical and practical caveats of image processing and how to overcome those with basic practical principles.

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Program

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
Statistical Analysis

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Course delivery

The course will include two theoretical classes. The remaining of the course will consist of practical sessions, with exercises to be completed and discussed during the sessions.

During the classes, the teacher with an assistant will give hints to the students to complete the tasks, which will be revised together.

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Learning assessment methods

The learning assessment is determined on the basis of the results of a series of laboratory, a final PowerPoint presentation in groups of maximum 3 people of a software tool chosen by the student (Image J plugin, or any other standalone software) that was not discussed during the laboratory, and a practical exam consisting in an excercise with the laptop.

The laboratory has compulsory attendance of 75%. Students can take the final exam based on the attendance obtained. The final evaluation (in thirtieths) includes the exercises carried out during the practical exam (60%) and the presentation (40%).

Suggested readings and bibliography

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There is no specific textbook for this course. For basic and general reference, see The Image Processing Handbook, 6th edition by J.C. Russ

https://www.amazon.com/Image-Processing-Handbook-Sixth/dp/1439840458

NeuroMorph: A Toolset for the Morphometric Analysis and Visualization of 3D Models Derived from Electron Microscopy Image Stacks
https://link.springer.com/article/10.1007/s12021-014-9242-5


A Method for 3D Reconstruction and Virtual Reality Analysis of Glial and Neuronal Cells
https://www.jove.com/it/t/59444/a-method-for-3d-reconstruction-virtual-reality-analysis-glial
 


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Class scheduleV

Lessons: from 02/03/2020 to 12/06/2020

Enroll
  • Closed
    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!)
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    Last update: 07/03/2024 12:32
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