NDLI: Registering richly labelled 3D images

Content Provider	IEEE Xplore Digital Library
Author	Babalola, K.O. Cootes, T.F.
Copyright Year	2006
Description	Author affiliation: Imaging Sci. & Biomedical Eng., Manchester Univ. (Babalola, K.O.; Cootes, T.F.)
Abstract	We propose a method of registering 3D images in which many regions have been segmented and labelled. Images in which some regions have been labelled can be registered by generating a vector valued image with a number of planes, one for each individual label class, and applying registration algorithms to the multi-plane images. However, when there are many labels such an approach can lead to impractically large images. We demonstrate that good results can be obtained by mapping each label value to a vector in a low dimensional space and applying a multi-plane registration algorithm to the resulting vector image. For the approach to work well, the vectors used for each label should be well separated, and chosen in such a way that there is minimal confusion between them. We demonstrate the method by using it to construct statistical shape models by applying a groupwise alignment method to a set of richly labelled 3D brain images
Starting Page	868
Ending Page	871
File Size	358487
Page Count	4
File Format	PDF
ISBN	078039576X
DOI	10.1109/ISBI.2006.1625056
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-04-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Biomedical imaging Biomedical engineering Brain modeling Interference Convergence Active shape model Mutual information Cost function
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Statistical Shape Model-based Segmentation of brain MRI Images

Implicit surface segmentation by minimal paths, applications in 3D medical images

3D MRI segmentation of brain structures

Shape gradient for multimodal image segmentation using mutual information

Statistical 3D shape-model guided segmentation of cardiac images

High effective medical image segmentation with model adjustable method

An Algorithm Embedded Prior Shape Information for Segmentation of Medical Image Series

Accurate and fully automatic 3D registration of spinal images using normalized mutual information

Model-based 3D Analysis Of MR Images Of The Brain

Registering richly labelled 3D images

Similar Documents

Statistical Shape Model-based Segmentation of brain MRI Images

Implicit surface segmentation by minimal paths, applications in 3D medical images

3D MRI segmentation of brain structures

Shape gradient for multimodal image segmentation using mutual information

Statistical 3D shape-model guided segmentation of cardiac images

High effective medical image segmentation with model adjustable method

An Algorithm Embedded Prior Shape Information for Segmentation of Medical Image Series

Accurate and fully automatic 3D registration of spinal images using normalized mutual information

Model-based 3D Analysis Of MR Images Of The Brain

Registering richly labelled 3D images