A robust mosaicing method with super-resolution for optical medical images

Mingxing Hu; Graeme Penney; Daniel Rueckert; Philip Edwards; Fernando Bello; Michael Figl; Roberto Casula; Yigang Cen; Jie Liu; Zhenjiang Miao; David Hawkes

doi:10.1007/978-3-642-15699-1_39

A robust mosaicing method with super-resolution for optical medical images

Mingxing Hu, Graeme Penney, Daniel Rueckert, Philip Edwards, Fernando Bello, Michael Figl, Roberto Casula, Yigang Cen, Jie Liu, Zhenjiang Miao, David Hawkes

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

11 Scopus citations

Abstract

Constructing a mosaicing image with a broader field-of-view has become an important topic in image guided diagnosis and treatment. In this paper, we present a robust feature-based method for video mosaicing with super-resolution for optical medical images. Firstly, outliers involved in the feature dataset are removed using trilinear constraints and iterative bundle adjustment, then a minimal cost graph path is built for mosaicing using topology inference. Finally, a mosaicing image with super-resolution is created by way of maximum a posterior (MAP) estimation and selective initialization. The proposed method has been tested with both endoscopic images from totally endoscopic coronary artery bypass surgery and fibered confocal microscopy images. The results showed our method performs better than previously reported methods in terms of accuracy and robustness to deformation and artefacts.

Original language	English
Title of host publication	Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings
Pages	373-382
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-642-15699-1_39
State	Published - 2010
Externally published	Yes
Event	5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 - Beijing, China Duration: 19 Sep 2010 → 20 Sep 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6326 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010
Country/Territory	China
City	Beijing
Period	19/09/10 → 20/09/10

Access to Document

10.1007/978-3-642-15699-1_39

Cite this

Hu, M., Penney, G., Rueckert, D., Edwards, P., Bello, F., Figl, M., Casula, R., Cen, Y., Liu, J., Miao, Z., & Hawkes, D. (2010). A robust mosaicing method with super-resolution for optical medical images. In Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings (pp. 373-382). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6326 LNCS). https://doi.org/10.1007/978-3-642-15699-1_39

Hu, Mingxing ; Penney, Graeme ; Rueckert, Daniel et al. / A robust mosaicing method with super-resolution for optical medical images. Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. pp. 373-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{42c9b696498749ee900cfff9a6f34fea,

title = "A robust mosaicing method with super-resolution for optical medical images",

abstract = "Constructing a mosaicing image with a broader field-of-view has become an important topic in image guided diagnosis and treatment. In this paper, we present a robust feature-based method for video mosaicing with super-resolution for optical medical images. Firstly, outliers involved in the feature dataset are removed using trilinear constraints and iterative bundle adjustment, then a minimal cost graph path is built for mosaicing using topology inference. Finally, a mosaicing image with super-resolution is created by way of maximum a posterior (MAP) estimation and selective initialization. The proposed method has been tested with both endoscopic images from totally endoscopic coronary artery bypass surgery and fibered confocal microscopy images. The results showed our method performs better than previously reported methods in terms of accuracy and robustness to deformation and artefacts.",

author = "Mingxing Hu and Graeme Penney and Daniel Rueckert and Philip Edwards and Fernando Bello and Michael Figl and Roberto Casula and Yigang Cen and Jie Liu and Zhenjiang Miao and David Hawkes",

year = "2010",

doi = "10.1007/978-3-642-15699-1_39",

language = "English",

isbn = "3642156983",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "373--382",

booktitle = "Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings",

note = "5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010 ; Conference date: 19-09-2010 Through 20-09-2010",

}

Hu, M, Penney, G, Rueckert, D, Edwards, P, Bello, F, Figl, M, Casula, R, Cen, Y, Liu, J, Miao, Z & Hawkes, D 2010, A robust mosaicing method with super-resolution for optical medical images. in Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6326 LNCS, pp. 373-382, 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010, Beijing, China, 19/09/10. https://doi.org/10.1007/978-3-642-15699-1_39

A robust mosaicing method with super-resolution for optical medical images. / Hu, Mingxing; Penney, Graeme; Rueckert, Daniel et al.
Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. p. 373-382 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6326 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A robust mosaicing method with super-resolution for optical medical images

AU - Hu, Mingxing

AU - Penney, Graeme

AU - Rueckert, Daniel

AU - Edwards, Philip

AU - Bello, Fernando

AU - Figl, Michael

AU - Casula, Roberto

AU - Cen, Yigang

AU - Liu, Jie

AU - Miao, Zhenjiang

AU - Hawkes, David

PY - 2010

Y1 - 2010

N2 - Constructing a mosaicing image with a broader field-of-view has become an important topic in image guided diagnosis and treatment. In this paper, we present a robust feature-based method for video mosaicing with super-resolution for optical medical images. Firstly, outliers involved in the feature dataset are removed using trilinear constraints and iterative bundle adjustment, then a minimal cost graph path is built for mosaicing using topology inference. Finally, a mosaicing image with super-resolution is created by way of maximum a posterior (MAP) estimation and selective initialization. The proposed method has been tested with both endoscopic images from totally endoscopic coronary artery bypass surgery and fibered confocal microscopy images. The results showed our method performs better than previously reported methods in terms of accuracy and robustness to deformation and artefacts.

AB - Constructing a mosaicing image with a broader field-of-view has become an important topic in image guided diagnosis and treatment. In this paper, we present a robust feature-based method for video mosaicing with super-resolution for optical medical images. Firstly, outliers involved in the feature dataset are removed using trilinear constraints and iterative bundle adjustment, then a minimal cost graph path is built for mosaicing using topology inference. Finally, a mosaicing image with super-resolution is created by way of maximum a posterior (MAP) estimation and selective initialization. The proposed method has been tested with both endoscopic images from totally endoscopic coronary artery bypass surgery and fibered confocal microscopy images. The results showed our method performs better than previously reported methods in terms of accuracy and robustness to deformation and artefacts.

UR - http://www.scopus.com/inward/record.url?scp=78049425467&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-15699-1_39

DO - 10.1007/978-3-642-15699-1_39

M3 - Conference contribution

AN - SCOPUS:78049425467

SN - 3642156983

SN - 9783642156984

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 373

EP - 382

BT - Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings

T2 - 5th International Workshop on Medical Imaging and Augmented Reality, MIAR 2010

Y2 - 19 September 2010 through 20 September 2010

ER -

Hu M, Penney G, Rueckert D, Edwards P, Bello F, Figl M et al. A robust mosaicing method with super-resolution for optical medical images. In Medical Imaging and Augmented Reality - 5th International Workshop, MIAR 2010, Proceedings. 2010. p. 373-382. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-15699-1_39

A robust mosaicing method with super-resolution for optical medical images

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this