CM2006 Medical Image Visualization 6.0 credits

·       Applications in medical image visualization

·       The visualization pipeline

·       Surface reconstruction and rendering

·       Volume rendering

·       Volume interaction

·       Stereoscopic techniques

The course consists of lectures, laboratories and mathematical exercises. The participants will combine VTK (the Visualization Toolkit) in Python with other libraries. The course also includes introductory labs for students with programming experience but without experience in Python.

Medical image visualization is a specific area of scientific visualization that is focused on medical applications. Visualization in a medical context is used for different purposes, including diagnosis with radiological data, treatment planning, intraoperative support, data annotation and educational purposes among others.

This course covers the concepts, theories and most used methods used for exploring and interacting with images in medical applications. After completion, the participant will be able to:

·       Understand the different parts of a visualization pipeline

·       Understand the theory of the most used methods for surface and volume rendering

·       Summarize the most used techniques in volume interaction and stereo rendering

·       Design visualization solutions for medical applications

·       Select and adapt the most appropriate methods for image visualization in medical applications

·       Create visualization prototypes using medical images that can be used in medical applications

in order to:

·       understand the complete visualization pipeline in a medical context

·       be able to implement visualization solutions in medical applications

·       have a broad knowledge base that can ease understanding literature in the field

Bachelor's degree in medical technology, technical physics, electrical engineering, computer science or equivalent. English 6/B.

Courses in programming in Python or MATLAB.

The suggested text book is:

1.     Bernhard Preim and Charl Botha. Visual Computing for Medicine, Theory, Algorithms and Applications 2nd Ed. Morgan Kaufman. 2014

Additional literature includes:

2.     Alexandru C Telea. Data Visualization: Principles and Practice 2nd Ed. CRC Press. 2015

3.     Will Schroeder, Ken Martin, Bill Lorensen. The Visualization Toolkit: An Object-Oriented Approach To 3D Graphics 4th Ed. Kitware Inc. 2006 (available online)

4.     The VTK User’s Guide 11th Ed. Kitware Inc. 2010 (available online)

5.      

If the course is discontinued, students may request to be examined during the following two academic years.

• PRO1 - Project, 6.0 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Approved project assignment and participation in at least 90% of the course activities.

Rodrigo Moreno

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.