Robust computation of mutual information using spatially adaptive meshes

Hari Sundar; Dinggang Shen; George Biros; Chenyang Xu; Christos Davatzikos

doi:10.1007/978-3-540-75757-3_115

Robust computation of mutual information using spatially adaptive meshes

Hari Sundar, Dinggang Shen, George Biros, Chenyang Xu, Christos Davatzikos

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

28 Citations (Scopus)

Abstract

We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings
Publisher	Springer Verlag
Pages	950-958
Number of pages	9
Edition	PART 1
ISBN (Print)	9783540757566
DOIs	https://doi.org/10.1007/978-3-540-75757-3_115
Publication status	Published - 2007
Event	10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007 - Brisbane, Australia Duration: 2007 Oct 29 → 2007 Nov 2

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	4791 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007
Country/Territory	Australia
City	Brisbane
Period	07/10/29 → 07/11/2

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-75757-3_115

Cite this

Sundar, H., Shen, D., Biros, G., Xu, C., & Davatzikos, C. (2007). Robust computation of mutual information using spatially adaptive meshes. In Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings (PART 1 ed., pp. 950-958). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4791 LNCS, No. PART 1). Springer Verlag. https://doi.org/10.1007/978-3-540-75757-3_115

Robust computation of mutual information using spatially adaptive meshes. / Sundar, Hari; Shen, Dinggang; Biros, George et al.
Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2007. p. 950-958 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4791 LNCS, No. PART 1).

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

Sundar, H, Shen, D, Biros, G, Xu, C & Davatzikos, C 2007, Robust computation of mutual information using spatially adaptive meshes. in Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 4791 LNCS, Springer Verlag, pp. 950-958, 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007, Brisbane, Australia, 07/10/29. https://doi.org/10.1007/978-3-540-75757-3_115

Sundar H, Shen D, Biros G, Xu C, Davatzikos C. Robust computation of mutual information using spatially adaptive meshes. In Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings. PART 1 ed. Springer Verlag. 2007. p. 950-958. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-540-75757-3_115

Sundar, Hari ; Shen, Dinggang ; Biros, George et al. / Robust computation of mutual information using spatially adaptive meshes. Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings. PART 1. ed. Springer Verlag, 2007. pp. 950-958 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

@inproceedings{4e4a19d840b346f0aac8157664d985ea,

title = "Robust computation of mutual information using spatially adaptive meshes",

abstract = "We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.",

author = "Hari Sundar and Dinggang Shen and George Biros and Chenyang Xu and Christos Davatzikos",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007 ; Conference date: 29-10-2007 Through 02-11-2007",

year = "2007",

doi = "10.1007/978-3-540-75757-3_115",

language = "English",

isbn = "9783540757566",

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

publisher = "Springer Verlag",

number = "PART 1",

pages = "950--958",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings",

edition = "PART 1",

}

TY - GEN

T1 - Robust computation of mutual information using spatially adaptive meshes

AU - Sundar, Hari

AU - Shen, Dinggang

AU - Biros, George

AU - Xu, Chenyang

AU - Davatzikos, Christos

PY - 2007

Y1 - 2007

N2 - We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.

AB - We present a new method for the fast and robust computation of information theoretic similarity measures for alignment of multi-modality medical images. The proposed method defines a non-uniform, adaptive sampling scheme for estimating the entropies of the images, which is less vulnerable to local maxima as compared to uniform and random sampling. The sampling is defined using an octree partition of the template image, and is preferable over other proposed methods of non-uniform sampling since it respects the underlying data distribution. It also extends naturally to a multi-resolution registration approach, which is commonly employed in the alignment of medical images. The effectiveness of the proposed method is demonstrated using both simulated MR images obtained from the BrainWeb database and clinical CT and SPECT images.

UR - http://www.scopus.com/inward/record.url?scp=84874403078&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-75757-3_115

DO - 10.1007/978-3-540-75757-3_115

M3 - Conference contribution

C2 - 18051150

AN - SCOPUS:84874403078

SN - 9783540757566

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

SP - 950

EP - 958

BT - Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings

PB - Springer Verlag

T2 - 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007

Y2 - 29 October 2007 through 2 November 2007

ER -

Robust computation of mutual information using spatially adaptive meshes

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this