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Localization of flexible surgical instruments inendoscopic images using machine learning methods
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The work presented in this document deals with Machine learning algorithms used in

surgical robotic problems, especially here in the Isis project. The aim of the project is

to replace manual handles for endoscopic operations by a set of motors commanded

via high-tech interface. The main aim of the thesis is to solve the problem of the

estimation of the pose of flexible surgical instrument, using only the video flux from

an endoscopic camera.

After a short introduction about the system its environment and the definition of the

pose problem, the work is divided in two chapters. Machine learning algorithms and

learning systems are used in both parts.

The first chapter deals with image processing and video tracking. Usage of colored

markers and how the learning is made to perform the best segmentation is explained.

It starts with simple linear classification to end with an Adaboost algorithm. The

learning database construction and all the challenges it raises are explained too.

Then the tracker used in the system is decomposed and its structure explained. The

results of the whole tracking system are presented at the end of the chapter.

The second chapter deals with function approximation: we build Radial Basis

Function networks in order to approximate the final position of the instrument (or its

mechanic parameters) from the data extracted by the tracking system. Learning

algorithms are used too. The learning training set is built in a laboratory environment

where the real position of the instrument can be measured.

The last chapter is a collection of improvements that could be added to the system

and opens on future perspectives about the project in general.

Place, publisher, year, edition, pages
2013.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-142470OAI: oai:DiVA.org:kth-142470DiVA: diva2:703037
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2014-03-11 Created: 2014-03-05 Last updated: 2014-03-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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