Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Zhong Xue; Dinggang Shen; Christos Davatzikos

doi:10.1109/TMI.2004.834616

Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Zhong Xue, Dinggang Shen, Christos Davatzikos

Research output: Contribution to journal › Article › peer-review

55 Citations (Scopus)

Abstract

Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) denned on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

Original language	English
Pages (from-to)	1276-1291
Number of pages	16
Journal	IEEE Transactions on Medical Imaging
Volume	23
Issue number	10
DOIs	https://doi.org/10.1109/TMI.2004.834616
Publication status	Published - 2004 Oct

Bibliographical note

Funding Information:
Manuscript received May 2, 2004; revised June 24, 2004. This work was supported by the National Institutes of Health (NIH) under Grant R01AG14971. The Associate Editor responsible for coordinating the review of this paper and recommending its publication was M. Sonka. Asterisk indicates corresponding author. *Z. Xue is with the Section of Biomedical Image Analysis (SBIA), Department of Radiology School of Medicine, University of Pennsylvania, 3600 Market ST Suite 380, Philadelphia PA 19104 USA (e-mail: [email protected]).

Keywords

Computational anatomy
Correspondence
Deformable registration
Image matching
Wavelet transformations

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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abstract = "Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) denned on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.",

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AB - Finding point correspondence in anatomical images is a key step in shape analysis and deformable registration. This paper proposes an automatic correspondence detection algorithm for intramodality MR brain images of different subjects using wavelet-based attribute vectors (WAVs) denned on every image voxel. The attribute vector (AV) is extracted from the wavelet subimages and reflects the image structure in a large neighborhood around the respective voxel in a multiscale fashion. It plays the role of a morphological signature for each voxel, and our goal is, therefore, to make it distinctive of the respective voxel. Correspondence is then determined from similarities of AVs. By incorporating the prior knowledge of the spatial relationship among voxels, the ability of the proposed algorithm to find anatomical correspondence is further improved. Experiments with MR images of human brains show that the algorithm performs similarly to experts, even for complex cortical structures.

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Determining correspondence in 3-D MR brain images using attribute vectors as morphological signatures of voxels

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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