1. Recognition of biological cells – the beginning of study
Marcin Skoczylas
Marcin Skoczylas
Bordeaux, February 2006

2. PREVIOUS STUDY Since 1990 1998 – 2003 2003 – 2005
Participating in various computer programming competitions
1998 – 2003
Technical University – Computer Science
Politechnika Białostocka, Poland
2003 – 2005
Movie and Television Academy – director diploma (hobby)

3. My first nanowire (with Takeshi Ohgai)
PREVIOUS EXPERIENCE
2002 – 2004 First contact with ion tracks. Software for etching and replicating single ion tracks at GSI Darmstadt, Germany
My first nanowire (with Takeshi Ohgai)
2004 – 2005 Developing software for a large Polish bank (BPH):
„online stock market” at COMARCH Kraków, Poland

4. TASKS
Development:
- Create software for automatic cell positioning - Resolve typical recognition problems such as overlapping cells, or cells with shape variations - Build fast algorithms
Study:
- Image processing techniques
- Neural Networks - Parallel programming

5. BASIC IDEAS Advanced image processing can be time consuming:
- Increase processing speed by CPU clustering
(parallel programming)
- Use Neural Networks for the recognition and classification
Note: Neural Networks are common practice in Optical Character Recognition (OCR), stained cells recognition, fingerprints.

6. HISTORY OF NEURAL NETWORKS
1892 Santiago Ramon y Cajal determined that the nervous system is built with discrete neurons. They communicate with each other by sending electrical signals using axons which touch the dendrites, transmitting the signal through synapses
1943 McCulloch and Pitts proposed the first computationel model of a neuron. Output was 0 or 1
1949
Donald Olding Hebb invented learning rule:
Synaptic connections and the strength of a synapse would increase by the repeated activation of one neuron by the other one

7. McCulloch-Pitts model of neuron
ARTIFICIAL NEURON
OUTPUT
McCulloch-Pitts model of neuron

8. HISTORY CONTINUES
1962 Frank Rosenblatt discovered first iterative learning procedure for single layer perceptron. Tapping a great potential.
1969
Marvin Minsky and Seymour Papert showed that single layer perceptron is very limited, it cannot learn simple functions like XOR
1982 Hopfield suggested that a neural network can be analyzed as energy function
1985
Ackley, Hinton & Sejnowski: Boltzman Machine
1986
David Rumelhart: Back-propagation learning

9. NEURAL NETWORKS
Back- propagation
learning
input
hidden
output
can form associations between arbitrary input and output patterns
don’t just memorize training data, they learn („understand”) the underlying patterns
are tolerant of both physical damage and noisy data
they can be implemented in the parallel-suited computer

10. ... ... SIMPLE RECOGNITION LEARNING: CLASSIFICATION:
1000
0100
0010
0001 1
...
...
LEARNING:
CLASSIFICATION:
Repeat many times for whole learning set: - run network, calculate output - compute output error - adjust neurons weight by a small value to get smaller output error
- Feed new data into Network’s inputs - Run network and calculate output

12. PREPARE THE RECOGNITION
IMAGE ANALYSIS:
- Search the best image filters combination
- Look for shapes
CONSTRUCT THE NEURAL NETWORK:
- Classify example cell pictures by human
- Experiment with the chosen neural network topology
- Adapt neural nodes weights using known learning rules
- Update database with new information
Experimental work:
Try to invent genetic algorithms (evolution) rule to obtain neural nodes weight
and the network topology.

13. RECOGNITION TASK
- Each cluster element will get data from the database (selected image filters, neural network topology and each neuron weight)
- Acquire pictures, split images between cluster elements
- Apply image filters
- Search for shapes, make feature extraction
- Classify shapes using neural network

14. GOAL ... LabView camera C++ interface to java (plugin) JAVA
main control
SQL
database
Fast ethernet connection!
Java
cluster element
Java
cluster element
Java
cluster element
...

15. DEVELOPMENT
JAVA
RMI
ECLIPSE

16. DO YOU HAVE ANY QUESTIONS?
THANK YOU
DO YOU HAVE ANY QUESTIONS?
Litereature: Rafael Gonzalez, Richard Woods: Digital Image Processing Robert Pandya, B. Macy: Pattern recognition with neural networks Joe Tebelskis: Speech recognition using Neural Networks, PhD thesis The Image of neuron comes from