| Home | << 1 >> |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva | ||||
| Title | Conditional Random Fields for image segmentation in Intravascular Ultrasound | Type | Conference Article | ||
| Year | 2010 | Publication | Medical Image Computing in Catalunya: Graduate Student Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | 13–14 | ||
| Keywords | |||||
| Abstract | We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved. | ||||
| Address | Girona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN  |
Medium | |||
| Area | Expedition | Conference | MICCAT | ||
| Notes | MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ CPF2010 | Serial | 1453 | ||
| Permanent link to this record | |||||
| Author | Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva | ||||
| Title | ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences | Type | Conference Article | ||
| Year | 2009 | Publication | 12th International Conference on Medical Image and Computer Assisted Intervention | Abbreviated Journal | |
| Volume | 5762 | Issue | II | Pages | |
| Keywords | |||||
| Abstract | The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers. | ||||
| Address | London, UK | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN |
978-3-642-04270-6 | Medium | |
| Area | Expedition | Conference | MICCAI | ||
| Notes | MILAB;HuPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ CPF2009 | Serial | 1228 | ||
| Permanent link to this record | |||||