Rigid registration of CT, MR and cryosection images using a GLCM framework

Morten Bro-Nielsen; J. Troccaz; E. Grimson; R. Mosges

Rigid registration of CT, MR and cryosection images using a GLCM framework

Morten Bro-Nielsen, J. Troccaz (Editor), E. Grimson, R. Mosges

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

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Abstract

The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from the GLCM literature are compared to the current range of measures using images from the visible human data set. The voxel-based rigid registration of cryosection and CT images have not been reported before. The tests show that mutual information is the best general measure, but some GLCM features are better for specific modality combinations. This paper discusses existing and some new voxel similarity measures for image registration. Elaborate tests are used to evaluate the different measures and compare them. Finally, a registration algorithm based on voxel similarity measures is described and some results are presented

Original language	English
Title of host publication	CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc
Publisher	Springer Verlag
Publication date	1997
Pages	171-180
Publication status	Published - 1997
Event	CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc - Duration: 1 Jan 1997 → …

Conference

Conference	CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc
Period	01/01/1997 → …

Keywords

medical image processing
image texture
matrix algebra
image registration
biomedical NMR
computerised tomography

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@inproceedings{34c96cd94d6845b1a3a30f11c3a0c007,

title = "Rigid registration of CT, MR and cryosection images using a GLCM framework",

abstract = "The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from the GLCM literature are compared to the current range of measures using images from the visible human data set. The voxel-based rigid registration of cryosection and CT images have not been reported before. The tests show that mutual information is the best general measure, but some GLCM features are better for specific modality combinations. This paper discusses existing and some new voxel similarity measures for image registration. Elaborate tests are used to evaluate the different measures and compare them. Finally, a registration algorithm based on voxel similarity measures is described and some results are presented",

keywords = "medical image processing, image texture, matrix algebra, image registration, biomedical NMR, computerised tomography",

author = "Morten Bro-Nielsen and J. Troccaz and E. Grimson and R. Mosges",

year = "1997",

language = "English",

pages = "171--180",

booktitle = "CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc",

publisher = "Springer Verlag",

address = "Germany",

note = "CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc ; Conference date: 01-01-1997",

}

Bro-Nielsen, M, Troccaz, J (ed.), Grimson, E & Mosges, R 1997, Rigid registration of CT, MR and cryosection images using a GLCM framework. in CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc. Springer Verlag, pp. 171-180, CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc, 01/01/1997.

Rigid registration of CT, MR and cryosection images using a GLCM framework. / Bro-Nielsen, Morten; Troccaz, J. (Editor); Grimson, E.; Mosges, R.

CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc. Springer Verlag, 1997. p. 171-180.

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

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T1 - Rigid registration of CT, MR and cryosection images using a GLCM framework

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AU - Mosges, R.

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N2 - The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from the GLCM literature are compared to the current range of measures using images from the visible human data set. The voxel-based rigid registration of cryosection and CT images have not been reported before. The tests show that mutual information is the best general measure, but some GLCM features are better for specific modality combinations. This paper discusses existing and some new voxel similarity measures for image registration. Elaborate tests are used to evaluate the different measures and compare them. Finally, a registration algorithm based on voxel similarity measures is described and some results are presented

AB - The majority of the available rigid registration measures are based on a 2-dimensional histogram of corresponding grey-values in the registered images. This paper shows that these features are similar to a family of texture measures based on grey level co-occurrence matrices (GLCM). Features from the GLCM literature are compared to the current range of measures using images from the visible human data set. The voxel-based rigid registration of cryosection and CT images have not been reported before. The tests show that mutual information is the best general measure, but some GLCM features are better for specific modality combinations. This paper discusses existing and some new voxel similarity measures for image registration. Elaborate tests are used to evaluate the different measures and compare them. Finally, a registration algorithm based on voxel similarity measures is described and some results are presented

KW - medical image processing

KW - image texture

KW - matrix algebra

KW - image registration

KW - biomedical NMR

KW - computerised tomography

M3 - Article in proceedings

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BT - CVRMed-MRCAS '97. 1st Joint Conf, Comp Vision, VR and Robotics in Medicine and Medical Robotics and Comp-Assisted Surgery Proc

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ER -

Rigid registration of CT, MR and cryosection images using a GLCM framework

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Keywords

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