Module description

Medical Engineering (MTM)

Medical Computer Science

Recommended prior knowledge

• Very good knowledge of the MATLAB script language, at best knowledge of a procedural or object-oriented programming language such as C, Java or Python
• Basic knowledge of the Unified Modeling Language (UML): Class diagrams, communication diagrams
• Basic knowledge of algorithms and data structures: sorting algorithms, list types
• Principles of Linar Algebra and Analytical Geometry

Teaching Methods Lecture/Seminar/Lab
Learning objectives / competencies

After successfully visiting this module
• the students have a mental map of two- and three-dimensional image processing,
• the students have learned to apply the principles of Linear Algebra and Analytical Geometry in a profitable way to the problem of three-dimensional image processing,
• the students are able to implement stereoscopic image processing systems,
• the students have in-depth knowledge of the programming language C++,
• the students are able to program complex algorithms in C++,
• the students have understood the principles of object-oriented programming and can apply them to their own problems,
• the students know the possibilities and limits, the advantages and disadvantages of the programming language C++,
• the students have learned to use C++ gainfully at a high level.

Duration 2
SWS 6.0
Effort
Classes 90h
Self-study / group work: 150h
Workload 240h
ECTS 8.0
Requirements for awarding credit points

Presentation / Laboratory Work / Two Exams (K60)

Credits and Grades

8 ECTS

Responsible Person

Prof. Dr.-Ing. Harald Hoppe

Recommended Semester 1-2
Frequency Annually (ss)
Usability

Master Course Medical Engineering

Lectures

Maschinelles Sehen mit Labor

Type Vorlesung/Labor
Nr. EMI2247
SWS 4.0
Lecture Content

Lecture contents:

Feature-based methods:

  • Feature detectors and feature descriptors
  • SIFT detector and descriptor

Image Transformations:

  • Affine and Projective Transformations
  • Robust transformation estimation (RANSAC)

Image Motion and Tracking

  • Visual odometry and optical flow (local and global methods)

Machine learning in image processing

  • Clustering/Segmentation: k-means, SLIC Superpixel, spectral methods
  • Classification: Support Vector Machines

Deep learning in machine vision

  • Fundamentals of deep neural networks in image processing (convolutional neural networks, CNNs)
  • Training and training data collection
  • Object classification with neural networks
  • Object detection and segmentation with neural networks

 Laboratory contents:

  • image mosaicing: image transformations and scale-invariant feature detectors
  • Visual Odometry: Non-contact speed determination in video sequences
  • Machine learning methods for segmentation: K-Means in image compression
  • Deep Learning: Object classification and detection
  • Deep Learning: Keras, Tensorflow and python-based open source usage

 Literature:

  • Szeliski, R., Computer Vision: Algorithms and Applications; Springer, 2011, online pdf version: http://szeliski.org/Book/
  • Burger, Burge, Digital Image Processing - An algorithmic introduction, 3rd ed. Springer, 2015
  • Gonzalez, Digital Image Processing, 4th ed., Pearson, 2017
  • Goodfellow, Bengio, Courville, Deep Learning, MIT Press 2016, onlineversion: http://www.deeplearningbook.org/

 

Literature
  • Szeliski, R., Computer Vision: Algorithms and Applications; Springer, 2011, online pdf version: http://szeliski.org/Book/
  • Burger, Burge, Digital Image Processing - An algorithmic introduction, 3rd ed. Springer, 2015
  • Gonzalez, Digital Image Processing, 4th ed., Pearson, 2017
  • Goodfellow, Bengio, Courville, Deep Learning, MIT Press 2016, onlineversion: http://www.deeplearningbook.org/

Dreidimensionale Bildverarbeitung

Type Vorlesung/Seminar
Nr. EMI2230
SWS 2.0
Lecture Content

• Analytical geometry for describing three-dimensional space, in particular rigid transformations and homogeneous coordinates
• Quaternions
• OpenGL transformations
• Stereoscopy and Photogrammetry: Camera Calibration, Epipolar Geometry, Rectification
• Landmarks, surface and voxel-based algorithms for the registration of three-dimensional image data sets
• Pixel, voxel, and edge-based segmentation algorithms
• Application of Voronoi diagrams and Delaunay triangulation in three-dimensional surface reconstruction
• Surface and volume rendering
• Hough transformation, distance transformation
• Wavelets
• Splines
• Selected algorithms of three-dimensional image processing (Marching Cubes Algorithm and others)

Literature

Handels, H., Medizinische Bildverarbeitung - Bildanalyse, Mustererkennung und Visualisierung für die computergestützte ärztliche Diagnostik und Therapie, Vieweg+Teubner Verlag, 2. überarbeitete und erweiterte Auflage, 2009

Schreer, O., Stereoanalyse und Bildsynthese, Springer, 2005

Jähne, B., Digitale Bildverarbeitung, Springer, 7. neu bearbeitete Auflage, 2012

Gonzalez, R. C., Woods, R. E., Digital Image Processing, Addison Wesley, 3rd International edition, 2008

Dougherty, G., Digital Image Processing for Medical Applications, Springer, 2011

Demant, C., Streicher-Abel, B., Springhoff, A., Industrielle Bildverarbeitung, Springer, 3. Auflage, 2011