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Image Analysis in Microscopy (a.k.a. Microscopy, Modeling and Visualization)

Vak 2013-2014

Admission requirements

Bachelor CS, LST, Biology, BPS …

Description

In this course the origin and analysis of images acquired through microscopy is the leading theme. Images play a major role in understanding of biological processes. Bio-molecular rocesses are visualized by a range of microscopical techniques and modalities. From images coherent visualizations and models are derived. The characteristic sequence of image analysis starts with the acquisition, proceeds to restoration and segmentation to conclude with analysis. This sequence will be the skeleton of this course. Image acquisition in microscopy will be dealt with on a theoretical as well as practical level. In a series of lectures all important aspects of imaging along the line of the characteristic sequence of image analysis are dealt with. Concepts of image processing will be introduced and it will be discussed how set of image features is compiled in measurements. Subjects will use the 2D imaging as a means of explaining the principles and the switch to multi-dimensional imaging to illustrate the implications of imaging in research and connect to current topics in bio-medical research. Presenting results through visualization and modeling is an ingredient found in applications that are discussed. The course consists of a series of lectures, practical assignments using programmable image analysis software environments and “hands-on” experience with microscopes (i.e. image acquisition). The course is concluded with a report on the practical work and a written exam.

Course objectives

At completion of the course, the student should be able to:

  • Understand the basic theory of Image Processing and Analysis.
  • Understand the principles of microscopy imaging and how to process and analyze images originating from a microscope.
  • Understand the basic algorithms for image processing and how to get to a measurement
  • Have gained insight in the conditions which should be fulfilled to obtain reliable measurements from images, especially microscope images
  • Have been exposed to software systems for image processing and analysis and understand the basic operational flow. Solve problems within a programmable software environment.
  • Have gained understanding of the mode of operation of a (automated) microscope and the meaning of the images it produces.

Timetable

The most recent timetable can be found at the LIACS website

Mode of instruction

  • Lectures
  • Practical Software
  • Practical Image Acquisition/Microscopy
  • Site Visits
  • Assignments

Assessment method

  • Written exam (Divided over two tests)
  • Report on Assignments
  • Mark = 50% Written Exam + 50% Report

Reading list

  • Digital Image Processing, 3rd edition, Rafael C. Gonzalez & Richard E. Woods, Publisher Prentice Hall, ISBN 0201180758
  • Papers made available on the website
  • Handouts from the lectures made available on the website.

Registration

You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

Admission requirements

No specific prerequisites required.

Description

Evolutionary Computation is a field of computer science dealing with algorithms gleaned from the model of organic evolution – so-called evolutionary algorithms. The idea is to let the computer evolve solutions to problems rather than trying to “calculate” them.

Evolutionary algorithms do this by using the fundamental principles of evolution such as, for example, selection, mutation and recombination among a population of simulated individuals. The evolutionary approach is used today in a variety of application areas for solving problems that require intelligent behaviour, adaptive learning and optimization. These fields include e.g. engineering optimization, artificial life, automatic programming, autonomous agents, and evolutionary economics.

Due to the large diversity of the field, the course focuses on the fundamentals of biological evolution as the underlying motivation, the main variants of evolutionary algorithms (genetic algorithms and evolution strategies), application examples, and some outlook into related aspects of evolutionary computation.

Course objectives

The course gives a comprehensive overview of the field through a series of lectures and exercises. In addition, a practical application exercise of evolutionary computation is given to the students, who are expected to run experiments and write a short report about the experiment and the results obtained. This report will be written in scientific paper format, and we will encourage and help the authors of the best student paper, provided that the quality of the results is sufficient, to submit this paper to a scientific conference.

Timetable

The most recent timetable can be found at the LIACS website

Mode of instruction

Lectures, mandatory werkcollege

Assessment method

The final grade is a combination of grades for
(1) the written exam (60%) and
(2) the report about the practical assignment (40%).

Reading list

  • The following books are recommended but not mandatory for the course:
    1) Th. Bäck: Evolutionary Algorithms in Theory and Practice, Oxford University Press, NY, 1996.
    2) A.E. Eiben, J.E. Smith: Introduction to Evolutionary Computing, Springer, Berlin, 2003.
  • Slides will be provided to the students for download.

Registration

You have to sign up for classes and examinations (including resits) in uSis. Check this link for more information and activity codes.

There is a limited capacity for students from outside the master Computer Science programme. Please contact the study advisor.

Contact information

Study coordinator Computer Science, Riet Derogee

Website

Website