liu.seSök publikationer i DiVA
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A Fast Optimization Method for Level Set Segmentation
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.
Linköpings universitet, Institutionen för teknik och naturvetenskap, Digitala Medier. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0002-6457-4914
Linköpings universitet, Institutionen för teknik och naturvetenskap, Digitala Medier. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0001-7557-4904
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan. Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV.ORCID-id: 0000-0002-9267-2191
2009 (Engelska)Ingår i: Image Analysis: 16th Scandinavian Conference, SCIA 2009, Oslo, Norway, June 15-18, 2009. Proceedings / [ed] A.-B. Salberg, J.Y. Hardeberg, and R. Jenssen, Springer Berlin/Heidelberg, 2009, s. 400-409Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Level set methods are a popular way to solve the image segmentation problem in computer image analysis. A contour is implicitly represented by the zero level of a signed distance function, and evolved according to a motion equation in order to minimize a cost function. This function defines the objective of the segmentation problem and also includes regularization constraints. Gradient descent search is the de facto method used to solve this optimization problem. Basic gradient descent methods, however, are sensitive for local optima and often display slow convergence. Traditionally, the cost functions have been modified to avoid these problems. In this work, we instead propose using a modified gradient descent search based on resilient propagation (Rprop), a method commonly used in the machine learning community. Our results show faster convergence and less sensitivity to local optima, compared to traditional gradient descent.

Ort, förlag, år, upplaga, sidor
Springer Berlin/Heidelberg, 2009. s. 400-409
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 5575
Nyckelord [en]
Image segmentation - level set method - optimization - gradient descent - Rprop - variational problems - active contours
Nationell ämneskategori
Teknik och teknologier
Identifikatorer
URN: urn:nbn:se:liu:diva-19313DOI: 10.1007/978-3-642-02230-2_41ISI: 000268661000041ISBN: 978-3-642-02229-6 (tryckt)ISBN: 978-3-642-02230-2 (tryckt)OAI: oai:DiVA.org:liu-19313DiVA, id: diva2:224261
Konferens
16th Scandinavian Conference on Image Analysis, June 15-18 2009, Oslo, Norway
Tillgänglig från: 2009-07-09 Skapad: 2009-06-17 Senast uppdaterad: 2018-01-23Bibliografiskt granskad
Ingår i avhandling
1. Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
Öppna denna publikation i ny flik eller fönster >>Segmentation Methods for Medical Image Analysis: Blood vessels, multi-scale filtering and level set methods
2010 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Image segmentation is the problem of partitioning an image into meaningful parts, often consisting of an object and background. As an important part of many imaging applications, e.g. face recognition, tracking of moving cars and people etc, it is of general interest to design robust and fast segmentation algorithms. However, it is well accepted that there is no general method for solving all segmentation problems. Instead, the algorithms have to be highly adapted to the application in order to achieve good performance. In this thesis, we will study segmentation methods for blood vessels in medical images. The need for accurate segmentation tools in medical applications is driven by the increased capacity of the imaging devices. Common modalities such as CT and MRI generate images which simply cannot be examined manually, due to high resolutions and a large number of image slices. Furthermore, it is very difficult to visualize complex structures in three-dimensional image volumes without cutting away large portions of, perhaps important, data. Tools, such as segmentation, can aid the medical staff in browsing through such large images by highlighting objects of particular importance. In addition, segmentation in particular can output models of organs, tumors, and other structures for further analysis, quantification or simulation.

We have divided the segmentation of blood vessels into two parts. First, we model the vessels as a collection of lines and edges (linear structures) and use filtering techniques to detect such structures in an image. Second, the output from this filtering is used as input for segmentation tools. Our contributions mainly lie in the design of a multi-scale filtering and integration scheme for de- tecting vessels of varying widths and the modification of optimization schemes for finding better segmentations than traditional methods do. We validate our ideas on synthetical images mimicking typical blood vessel structures, and show proof-of-concept results on real medical images.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2010. s. 44
Serie
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1434
Nyckelord
Image segmentation, Medical image analysis, Level set method, Quadrature filter, Multi-scale
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
urn:nbn:se:liu:diva-54181 (URN)LIU-TEK-LIC-2010:5 (Lokalt ID)978-91-7393-410-7 (ISBN)LIU-TEK-LIC-2010:5 (Arkivnummer)LIU-TEK-LIC-2010:5 (OAI)
Presentation
2010-04-15, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 13:00 (Engelska)
Opponent
Handledare
Tillgänglig från: 2010-04-20 Skapad: 2010-03-01 Senast uppdaterad: 2018-01-12Bibliografiskt granskad

Open Access i DiVA

fulltext(615 kB)1488 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 615 kBChecksumma SHA-512
733f1ab11a37d0428db7aef0a623e3d2064795daa062f9d38359c27d6785d6ac0dd77e5c9cfcc34b4961a861e8f28b2f2f2d79c293a38c27e8391e5715b11892
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltext

Personposter BETA

Andersson, ThordLäthén, GunnarLenz, ReinerBorga, Magnus

Sök vidare i DiVA

Av författaren/redaktören
Andersson, ThordLäthén, GunnarLenz, ReinerBorga, Magnus
Av organisationen
Medicinsk informatikTekniska högskolanCentrum för medicinsk bildvetenskap och visualisering, CMIVDigitala Medier
Teknik och teknologier

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 1488 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 1176 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf