Embed Size (px)
Citation preview
Chair for Computer Aided Medical Procedures & Augmented Reality
Department of Computer Science | Technische Universität München
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Automatic Feature Generation for Endoscopic Image Classification
Ulrich Klank
Nicolas PadoyProf. Nassir Navab
Supervisor:Advisor:
18 January 2007
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 2
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Overview
Endoscopic images Differences Similarities
Image feature generation using Genetic Programming A low level approach A high level approach
An example
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 3
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Endoscopic Images of two Surgical Phases: OP3
Images from the cutting and clipping phase (OP3) Images from the bag retraction phase (OP3)
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 4
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
…: OP1
Images from the bag retraction phase (OP1)Images from the cutting and clipping phase (OP1)
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 5
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Low-level Approach - Short Reminder
Genetic Programming: combination of low-level operators
PIXEL(With Parameters)
PUSH …FOR(With Parameters)
MUL LOAD(With Parameters)
ADD
Mutation
EvaluationCode
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 6
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Low-level Approach - Results
Distributed evaluation of programs on several computers (up to 7)
Nearly 10.000 programs evaluated (~300 generations)
First results: Characteristics of the best programs:
returning a short vector in a short time
Classification rate with a linear classifier is 62% (64 images of 2 phases of 4 videos)
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 7
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Problems with the low-level Approach
~40% of the programs contain major errors like Infinite running time, stack overflow No reference to the input image
Resulting programs still has structural similarity to the initial program. More generations needed
Evaluation of a programs is very slow due to the simulation of basic instructions on images
How to improve this method?
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 8
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Existing Software: GENIE
GENIE Software published by: Los Alamos National Laboratories First publication ’97, Commercial version in development
Genetic Programming for segmentation of images
Application example: Segmentation of Medical Images
Using a Genetic Algorithm by Payel Ghosh, Melanie Mitchell (’06)
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 9
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
The Step to a higher level approach
Erode(With Parameters)
Histogram(With Parameters)
MinLocOriginal CannyEdge(With Parameters)
Gradient x(With Parameters)
Dilate(With Parameters)
PIXEL(With Parameters)
PUSH …FOR(With Parameters)
MUL LOAD(With Parameters)
ADD
Replace the basic commands in a program by higher level operators:
Examples for low-level operator :
Examples for high-level operator :
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 10
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Evaluation of a Program
Semantic checks Input referred? No infinite loops?
Execution with several inputs 16 images per phase 2 phases per video at the moment 4 videos used for evaluation
A fitness function with 2 components: A classification of the phases by the output vectors The average execution time per input
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 11
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Genetic Concept – Cross Over
Erode(With Parameters)
Histogram(With Parameters)
MinLocOriginal CannyEdge(With Parameters)
Gradient x(With Parameters)
Dilate(With Parameters)
Gauss(With Parameters)
Original MaxOriginal PushImage(With Parameters)
Gradient y(With Parameters)
Histogram(With Parameters)
Program 1
Program 2
New Program
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 12
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
High-level Approach Benefits
Faster evaluation Reduced number of commands Optimized basic image operations (OpenCV)
Resulting programs easier to understand
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 13
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Current Results:
Running time: 480 ms (in simulation)
Output length: a vector of 48 signed integer
Classification rate: 67%
Rate based on 512 testing images out of 4 videos and 2 phases
Number of generations needed: ~80
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 14
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Future Work
Completion of the available image operators
Extension to multi-phases classification
Comparison of the fitness function with a standard classifier
Comparison with several standard features
Features evaluation within the workflow segmentation system
CAMP | Department of Computer Science | Technische Universität München | 19 April 2023 15
Chair for Computer Aided Medical Procedures & Augmented Reality | wwwnavab.cs.tum.edu
Thank you for your attention!
