Diploma thesis
[Subtitle] Genetic Algorithms in Vision
Thesis by: Ulrich Klank
Advisor: Nassir Navab
Supervision by: Nicolas Padoy
Due date: 15th of May 2007
Abstract
This work addresses the recognition of phases in minimally invasive surgeries. These
phases are sequential in time and should be recognized using observations which are acquired
automatically. The endoscopic view of the surgeon provides information about the
surgical field. This work focuses on the recognition of surgical phases using image features
computed from these endoscopic images. It is a complex task to select efficient features and
few literature exists about features that discriminate surgical phases in endoscopic views.
In this work, a new Genetic Programming approach is proposed to automate the search
for efficient features. A feature is modeled as a program and those programs are evolved
to improve the recognition rate. For the representation of the programs a programming language
was defined, specialized on computation of image features. Programs of this programming
language are evaluated by executing them on a virtual machine with labeled sample
images as input. Once the programs are evaluated and achieved a so called fitness, the best
programs are selected as parents for a slightly changed and probably improved new generation
of programs.
Finally, the resulting features are compared with several standard image features, to
show their performance in distinguishing between two phases using an image.With a selection
of the best features, a multi-class classifier is built. It is compared with an early approach
which is based on a neural network fed with a set of standard image features.
entia non sunt multiplicanda praeter necessitatem
(entities should not be multiplied beyond necessity)
– William of Ockham, 14th century (Ockham’s razor)
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