EU Marie Curie Initial Training Newtwork 
BME
8DM00 and ASCI course a8:
FrontEnd Vision
MultiScale and
MultiOrientation Image
Analysis
Fall 2015
An introduction to modern multiscale and
multiorientation image analysis 
Tutors: 
Prof. Bart M. ter Haar
Romeny, Eindhoven University of Technology / Northeastern University
(Shenyang, China) Prof. Nicolai Petkov, University of Groningen 
Dates:  2 fulltime weeks. Lectures (half day) from Monday 9 November 2015 till Friday 13 November 2015, and from Monday 16 November 2015 till Thursday 19 November 2015. Computer laboratories (other half day) using Mathematica 10 to exercise the course material and tasks. 
Registration: 
Register through the ASCI website:
here.
NonASCI members send an email to: R.A.v.Dijck.Postma@tue.nl. TU/e BME students register through the regular TU/e OASIS page for 8DM00. Registration for course and exam is free for TU/e, ASCI, NFBIA, ImagO students, and employees of industries officially collaborating with TU/e BME. For registration as 'contractant' with TU/e to do an official exam for 3 ECTS as nonTU/e student: see STU registration form. Costs: € 500 per course. Costs for industrial participants: € 1200 (invoice will be sent by ASCI after registration). 
Venue  Campus of Eindhoven University of Technology, Eindhoven, the
Netherlands. Google Maps, TU/e campus map. 
Hotel  Suggestions (Booking.com) 
Total duration: 27 oral lectures of 45
minutes each, and 27 hours handson training.
Code ASCI: a8 (4 ECTS study points).
Code TUEBME:
8D010
(2.5 ECTS study points).
Code MANET: Training (4 ECTS study
points).
Description:
Image analysis is the extraction of useful information from images. When we need to define a task on an image, in order to detect, enhance, register, recognize etc, in other words in order to translate the question of the clinical expert into an algorithm, we need a language for image analysis. In this course we give a modern mathematical (and physics based) approach to such a language: multiscale differential geometry for (medical) image analysis as a branch of computer vision.
We give an intuitive
introduction to multiscale image analysis, trying to keep the analogy with
stages in the human visual system as close as possible. The human visual system also widely exploits a
diversity of multiscale filters in its processing layers. We will treat the
frontend visual system in depth, especially its receptive field structures in
retina and primary visual cortex, and put emphasis on the Gaussian derivative
and the Gabor model for simple cortical receptive fields.
We study in detail the regularized measurement of derivatives by an early vision
system, and its inherent multiscale structure, and why this is necessary. The
discovery of the socalled pinwheel structure in the cortical columns inspired a
wellfounded multiorientation image analysis, which gives deeper insight in
contextual processing.
You will learn how geometric reasoning works and can be applied. We design image analysis algorithms by carefully studying the requirements, physical analogies, and in particular by looking how our visual system does it. After all, this is still the best performing recognition computer we know, even for noisy, partly missing (occluded) data, low contrast etc. Modern (often optical) brain imaging methods will be discussed (voltage sensitive dyes, optogenetics, fMRI, DTI/HARDI) and recent discoveries of functional brain mechanisms in visual perception.
Among the topics covered are: highorder derivative filters for 2D and 3D images, detecting edges, ridges, corners, Tjunctions etc. in images, multiscale analysis of 2D and 3D shape and motion from image sequences, depth from stereo, multiorientation analysis for contextual operations, and the use of contemporary, wellunderstood mathematical tools from differential geometry and tensor analysis (differential invariants, coordinate transformations, gauge coordinates).
The majority of the examples discussed are from 2D, 3D and 4D (3Dtime) medical imaging. We devote some time to the efficient numerical implementation of the different techniques. Handson experience is acquired in a computer lab. We use Mathematica 10 as this software suite is eminently suited for this design process, and we experiment handson with virtually all aspects discussed in the course.
Computer vision plays an increasing role in the detection and recognition of structures, quantitative analysis, segmentation and visualization.
A modern development is computeraided diagnosis (CAD), where the computer assists in finding possible pathology in images, particularly for screening applications. We discuss several examples in detail:
Detailed program and content:
Times: 
 
*Lecture rooms at TU/e campus, see map: FLX: Flux building (Applied Physics & Electrical Engineering) GEMZ: Gemini South building (Biomedical Engineering & Mechanical Engineering) GEMN: Gemini North building (same entrance as GeminiSouth). HEO: HelixEast bulding (ST  Chemical Technology) HEW: HelixWest building (ST  Chemical Technology) Matrix: Matrix building. 
The reader consists of the chapters of a the book: "FrontEnd Vision and Multiscale
Image Analysis", by Bart M. ter Haar Romeny. This book is written as a series of
Mathematica notebooks. It contains a CRROM
with all notebooks, which can be installed in the Mathematica Helpbrowser. The
Mathematica code is the
topic for the computer laboratories during the course. ISBN: 1402015070 (paperback), 1402015038 (hardcover). Springer,
Berlin. 
Recommended reading (books):
Recommended reading (websites):
Proceedings of all "ScaleSpace & Variational Methods" conferences:
Software:
Other:
Computer laboratories will be organized to acquire handson experience with all discussed topics on a variety of 2D and 3D images. We use the program Mathematica 10 (www.wolfram.com).
For TUE members: Mathematica 10 for
Windows can be
downloaded from the
TU/e campus software website.
Mac OS x version: \\physstor\appl\macsoftware.
Linux version:
\\wtbfiler\Software\UnixSoftware.
Recommended tutorial books on Mathematica:
Some useful notebooks:
The famous mathematics teaching files and resource online: MathWorld.
Select any three (3) questions from this set of questions: ExamTasks_FEV2015.nb.
Write a Mathematica 10 notebook per question and send them,
preferably within two weeks
after the end of the course, to B.M.terHaarRomeny@tue.nl.
Please explain the steps of your reasoning in detail, use Manipulate functions
if appropriate.
Make sure the notebook can run, so include your own images (store them in the
same directory as the notebook, and use SetDirectory[NotebookDirectory[ ]]), or
Import them from a web URL.
The detection of ridges (midlines) for an Xray image of hands. "Ridgeness" is a second order property. 
Low dose fluoroscopy image of an electrophysiology catheter in ther heart. The extra low dose is beneficial for the radiation dose, but leads to a deteriorated image quality.  Robust catheter detection with oriented filters and tensor voting. 
Left: histological image of a fungus cell, paramecium caudatum.

Noisy 2photon microscopy image of bone tissue. 
The same image enhanced with an orientationscore denoising filter. 
prof. Bart M. ter Haar Romeny,
PhD Department of Biomedical Engineering (BMEBMT) Group Biomedical Imaging Analysis BMIA Eindhoven University of Technology De Rondom 70  GEMZ 2.106 NL5612 AP Eindhoven, Netherlands Tel. +31402475537 (secr. Rina van Dijck) email: B.M.terHaarRomeny@tue.nl 
prof. Nicolai Petkov, PhD Department of Computing Science Rijks Universiteit Groningen Blauwborgje 3 NL9700 AV Groningen, Netherlands Tel: 0031503633939 (secr.) / 3637129 / 3633931 Fax: +31503633800 Email: N.Petkov@rug.nl 
Click for larger version.
Class of Fall 2015.