BMT 8DM00 and ASCI course a8, Fall 2017:

NEW: Exam tasks now available

Prof. Romeny retires
Symposium + Valedictory lecture 1 Dec 2017
Auditorium TU/e, 10:00-15:00, 16:00-17:00

Info and registration here (register today!)

A PhD / MSc course given at the Department of Biomedical Engineering of Eindhoven University of Technology.


Deep learning, self-organization and plasticity, convolutional neural networks, geometry engine, contextual Gestalt processing: the field known as 'brain-inspired computing' is one of the most promising avenues in medical image computing today.
To make new breakthroughs in this spectacular arena, the interplay between the fields of brain imaging and physiology, neural network informatics and fundamental mathematics is essential. However, these fields speak different languages, and interaction is nontrivial.
This course focuses on exactly that: we will discuss in detail modern findings in the neurophysiology, connectivity and functionality of the visual system, the best studied brain function today. And we look into the mathematical background of Deep Learning. The goal is to develop highly effective and efficient medical computer-aided diagnosis systems.

This is an intensive course of two full weeks (with a break in between), where each half day of theory is followed by a computer lab (all software code is supplied). We exercise with the developed notions, exploiting the high-level 'play and design' functionality of Mathematica 11.

The lecturers have extensive experience in the field, and are known for their excellent teaching.

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 (each half day) from Monday 20 November 2017 till Friday 24 November 2017, and from Tuesday 5 December 2017 till Friday 8 December 2017. Computer laboratories (other each half day) using Mathematica 11.


Register through the ASCI website: here.
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 non-TU/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 (

Total duration: 27 oral lectures of 45 minutes each, and 27 hours hands-on training.

Code ASCI: a8 (4 ECTS study points).
Code TUE-BME: 8DM00 (2.5 ECTS study points).
Code MANET: Training (4 ECTS study points).


We give insight in modern approaches to deep convolutional neural nets, and we will teach a number of well-established mathematical modeling techniques in detail, in particular multi-scale and multi-orientation differential geometry, models for self-organization and plasticity, and geometric neural feedback, leading to effective adaptive operations. We present the theory in an axiomatic, intuitive and fundamentally understood way.

This interactive course is interspersed with working and powerful applications in medical image analysis, such as computer-aided detection of breast tumors, invariant feature detection, development of retinal vessel biomarkers, and contextual Gestalt-based operators to deal with missing data.

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, opto-genetics, fMRI, DTI/HARDI) and recent discoveries of functional brain mechanisms in visual perception.

The majority of the examples discussed are from 2D, 3D and 4D (3D-time) medical imaging. We devote some time to the efficient numerical implementation of the different techniques. Hands-on experience is acquired in a computer lab. We use Mathematica 11 as this software suite is eminently suited for this design process. We experiment hands-on with virtually all aspects discussed in the course.

Some applications discussed:

Detailed program and content:

Day Time Content Lecture material Lecture hall
(see campus map)
20 November 2017
13:45-14:30 Course Introduction to brain-inspired computing

Observations, scale, low- and high-level vision tasks

  Introduction (ppt)

Introduction (PDF)

Powers of 10 


GEM-Z 3A-13
14:45-15:30 Course Axiomatics of multi-scale operators   Gaussian aperture from entropy (NB: MMA 8)
Gaussian aperture from entropy

(supplementary material)

GEM-Z 3A-13
15:45-16:30 Course Deep learning Deep learning part I

Deep learning (by G.Hinton)

GEM-Z 3A-13
21 November 2017
09:45-10:30 Course The Gaussian kernel Gaussian kernel

Dither removal

GEM-Z 3A-08
10:30-10:35 Group picture taken   GEM-Z 3A-08
10:45-11:30 Course Gaussian derivatives Gaussian derivatives


GEM-Z 3A-08
11:45-12:30 Course Introduction to Mathematica 11 Wolfram: Hands-on start

Wolfram: learning Center

Tutorial Mathematica notebooks of TU/e course 8ZZ16:

Course part 1 of 3 (English)
Course part 2 of 3 (English)

Course part 3 of 3 (English)

BMIA MMA 11 course

Image Processing with Mathematica 11 (by Markus van Almsick, Wolfram Research Inc.)

GEM-Z 3A-08
13:45-14:30 Computer lab

Fan Huang
Samaneh Abbasi

Exercises with Mathematica 11 Download the FEV book (will be updated soon)

MathVisionTools (containing an extensive package for Gaussian derivatives):

(Run the command:
in Mathematica, and store in this directory)

Images of the book & test images
(, 31 MB, extract in MathVisionTools\Data directory)


Tasks I (and answers)

Some testimages to play with.

GEM-Z 3A-06
14:45-15:30 Computer lab
Exercises with Mathematica 11 Additional study material:
Eindhoven Tips



Dictionary manipulations
Often used commands

FrondEnd Interactivity
Demo active shape
Wolfram Inc.
Eduroam (WiFi network access at TU/e through SurfNet)

GEM-Z 3A-06
15:45-16:30 Computer lab

Exercises with Mathematica 11

(Rehearse from first year 8C120:


GEM-Z 3A-06
22 November 2017
09:45-10:30 Course Deblurring Deblurring
Deblurring  PDF
GEM-Z 3A-08
10:45-11:30 Course Invariant gauge coordinates

Second order differential structure

Differential structure

Paper: B.M. ter Haar Romeny, A Geometric Model for the Functional Circuits of the Visual Front-End. Lecture Notes in Computer Science, vol. 8603, pp 35-50, 2014.

GEM-Z 3A-08
11:45-12:30 Course Affine corner detection   GEM-Z 3A-08
13:45-16:30 Computer Lab Exercising FEV lectures Tasks 02

Some answers Tasks 02

FLUX 1.07
23 November 2017
09:45-10:30 Course Front-end visual system I Front-End Vision.pptx


10:45-11:30 Course Front-end visual system II Youtube: Hubel & Wiesel - Cortical neuron V1

Youtube: Hubel's research

Youtube: Hubel: Brain & Perception

11:45-12:30 Course Front-end visual system III Visual illusions
(Michael Bach)

Visual illusions
Spiral illusion (Win32) Spiral illusion (Youtube)

13:45-16:30 Computer Lab Exercising FEV lectures Tasks 03

Tasks 03 (with solutions)

Tasks 04

Tasks 04 (with solutions)

24 November 2017
09:45-10:30 Course Limits on observations

Implementation of Gaussian convolutions


Limits   PDF


10:45-11:30 Course Regularization

Applications of second order differential structure

Regularization  PDF

Applications of second order structure


Original paper: A. Frangi, W. Niessen, K. Vincken and M. Viergever, Multiscale vessel enhancement filtering. In: Proceedings of the MICCAI’98Lecture Notes in Computer Science vol. 1496, Springer-Verlag, Berlin (1998), pp. 130–137. 


11:45-12:30 Course Third order differential structure   VERTIGO 8.08
13:45-16:30 Computer Lab Exercising FEV lectures   GEM-Z 3A.06
5 December 2017
09:45-10:30 Course Receptive fields from eigenpatches

Deep learning and convolutional neural nets



Brain development: the experiments of Blakemore and Cooper.

Deep learning part II

Wolfram MNIST digit classification (nb, pdf)

Deep Neural Nets and Image Processing in Mathematica 11 (zip, 1.3GB)

GEM-Z 3A.08
10:45-11:30 Course Geometry-driven diffusion Geometry-Driven Diffusion
Geometry-Driven Diffusion

Geometry-Driven Diffusion (PDF)

PDF: Original paper:
P. Perona, J. Malik, "Scale-space and edge detection using anisotropic diffusion", PAMI 12(7), pp. 629-639, 1990. 

GEM-Z 3A.08
11:45-12:30 Course Scale-time: differential structure of time sequences Scale-time

J. J. Koenderink, Scale-time, Biological Cybernetics
November 1988, Volume 58, Issue 3, pp 159-162.

GEM-Z 3A.08
13:45-16:30 Computer Lab Exercising FEV lectures Tasks - visual system GEM-Z 3A.06
6 December 2017
09:45-10:30 Course Color Differential Structure Color differential structure
Color differential structure

Color Invariants

Paper: Color RF

GEM-Z 3A.08
10:45-11:30 Course Deep structure I:
Edge focusing, watershed segmentation,
ScaleSpaceViz demo
Edge focusing, follicles

ScaleSpaceViz (VTK application) download

GEM-Z 3A.08


Deep structure II:

Toppoints, image retrieval
Follicle detection

Edge focusing, follicles

Toppoints in image retrieval

Topological numbers

GEM-Z 3A.08
13:45-16:30 Computer Lab Exercising FEV lectures, exam tasks   FLUX 1.07
7 December 2017
09:45-12:30 Computer Lab Exercising FEV lectures, exam tasks   GEM-Z 3A.06
13:45-14:30 Course Steerable kernels, stellate tumor detection Steerable kernels GEM-Z 3A.08
14:45-15:30 Course Multi-orientation analysis, context


Franken, E.M., Duits, R., ter Haar Romenij, B.M., Nonlinear diffusion on the 2D Euclidean motion group. Lecture Notes in Computer Science, Vol. 4485, pp. 461-472, 2007.(pdf)

Multi-orientation analysis GEM-Z 3A.08
15:45-16:30 Course Application of orientation analysis: RetinaCheck


Bekkers, E.J., Duits, R., Berendschot, T.T.J.M. & ter Haar Romenij, B.M. (2014). A multi-orientation analysis approach to retinal vessel tracking. Journal of Mathematical Imaging and Vision, 49(3), 583-610.(pdf)

RetinaCheck (new)

GEM-Z 3A.08
8 December 2017
09:45-12:30 Computer Lab Exercising FEV lectures

Tasks 05

Tasks 05 (with some solutions)

FLUX 1.07
13:45-14:30 Course V1-inspired orientation selective filters for image processing and computer vision (Gabor and CORF)

G. Azzopardi, N. Petkov: A CORF computational model of a simple cell that relies on LGN input outperforms the Gabor function model. Biological Cybernetics, 106 (3), 2012, 177-189 (pdf)

Azzopardi, G., Strisciuglio, N., Vento, M. and Petkov, N.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical image analysis, 19 (1): 46-57, 2016. (pdf)

Azzopardi, G., Rodriguez-Sanchez, A., Piater, J., Petkov, N.: A Push-Pull CORF Model of a Simple Cell with Antiphase Inhibition Improves SNR and Contour Detection. Plos one, 9 (7), article nr. 98424, 13 pages, 2014.(pdf)

Azzopardi, G., Petkov, N: Ventral-stream-like shape representation: from pixel intensity values to trainable object-selective COSFIRE models. Frontiers in Computational Neuroscience, vol. 8, nr. 80, 1-9, 2014.(pdf)

Prof. Nicolai Petkov

Lecture slides 01 (Orientation selectivity)

GEM-Z 3A.08
14:45-15:30 Course Surround suppression (or non-classical receptive field inhibition in areas V1/V2)

C. Grigorescu, N. Petkov, M.A. Westenberg, Contour and boundary detection improved by surround suppression of texture edges, Image and Vision Computing 22, 609–622, 2004. (pdf)

C. Grigorescu, N. Petkov, M.A. Westenberg, Contour detection based on nonclassical receptive field inhibition, IEEE Tr. on Image Processing, vol. 12, 7, 729-739, 2003 (pdf)

N. Petkov, M.A. Westenberg, Suppression of contour perception by band-limited noise and its relation to non-classical receptive field inhibition, Biol. Cybern. 88, 236–246 (2003) (pdf)

Prof. Nicolai Petkov


Lecture slides 02 (surround suppression)

GEM-Z 3A.08
15:45-16:30 Course What comes after V2? Computational model of shape selective neurons in area V4, with application to the detection of blood vessel bifurcations in retinal funds images.

G. Azzopardi, N. Petkov: Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 35 (2), pp. 490-503, 2016. (pdf)

Prof. Nicolai Petkov

Lecture slides 03
(CORF keypoint detectors)

GEM-Z 3A.08
*Lecture rooms at TU/e campus, see map:

FLX: Flux building (Applied Physics & Electrical Engineering)
GEM-Z: Gemini South building (Biomedical Engineering & Mechanical Engineering)
GEM-N: Gemini North building (same entrance as Gemini-South).
HEO: Helix-East bulding (ST - Chemical Technology)
HEW: Helix-West building (ST - Chemical Technology)
Matrix: Matrix building.


The reader consists of the chapters of a the book: "Front-End Vision and Multi-scale Image Analysis", by Bart M. ter Haar Romeny. This book is written as a series of Mathematica notebooks. It contains a CR-ROM with all notebooks, which can be installed in the Mathematica Help-browser. The Mathematica code is the topic for the computer laboratories during the course.

ISBN: 1-4020-1507-0 (paperback), 1-4020-1503-8 (hardcover). Springer, Berlin. 
Order the book with Springer or Amazon.

Recommended reading (books):

Recommended reading (websites):



Old video lectures (2011):

Computer Laboratory:

Computer laboratories will be organized to acquire hands-on experience with all discussed topics on a variety of 2D and 3D images. We use the program Mathematica 11 (

For TUE members: Mathematica 11 for Windows can be downloaded from the TU/e campus software website.
Mac OS x version:
Linux version:

Recommended introductory 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_FEV2017.nb.

Write a Mathematica 11 notebook per question and send them, preferably within two weeks after the end of the course, to
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.

Some examples

The detection of ridges (midlines) for an X-ray image of hands. "Ridgeness" is a second order property.

Low dose fluoroscopy image of an electro-physiology 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.
Middle: edge detection with a detector optimized for yellow-blue differences.
Right: edge detection with a detector optimized for red-green differences.

Noisy 2-photon microscopy image of bone tissue.

The same image enhanced with an orientation-score denoising filter.

Contact the tutors:

prof. Bart M. ter Haar Romeny, PhD
Department of Biomedical Engineering (BME-BMT)
Group Biomedical Imaging Analysis BMIA
Eindhoven University of Technology
De Rondom 70 - GEM-Z 2.106
NL-5612 AP Eindhoven, Netherlands
Tel. +31-40-2475537 (secr. Rina van Dijck)


prof. Nicolai Petkov, PhD
Department of Computing Science
Rijks Universiteit Groningen
Blauwborgje 3
NL-9700 AV Groningen, Netherlands
Tel: 00-31-50-3633939 (secr.) / 3637129 / 3633931
Fax: +31-50-3633800


Click for larger version.

Class of Fall 2017.