NDLI: Local intensity model: An outlier detection framework with applications to white matter hyperintensity segmentation

Content Provider	IEEE Xplore Digital Library
Author	Raniga, P. Schmitt, P. Bourgeat, P. Fripp, J. Villemagne, V.L. Rowe, C.C. Salvado, O.
Copyright Year	2011
Description	Author affiliation: The Mental Health Research Institute, University of Melbourne, Parkville, VIC, Australia (Villemagne, V.L.) \|\| Department of Nuclear Medicine and Centre for PET, and Department of Medicine, University of Melbourne, Austin Hospital, Melbourne, VIC, Australia (Rowe, C.C.) \|\| CSIRO Preventative Health National Research Flagship ICTC, The Australian e-Health Research Centre-BioMedIA, Royal Brisbane and Women's Hospital, Herston, QLD, Australia (Raniga, P.; Schmitt, P.; Bourgeat, P.; Fripp, J.; Salvado, O.)
Abstract	Automatic segmentation of white matter hyperintensities (WMH) from T2-Weighted and FLAIR MRI is a common task that needs to be performed in the analysis of many different diseases. A method to segment the WMH is proposed whereby a local intensity model (LIM) of normal tissue is generated. WMH are detected as outliers from this model. The LIM enables an accurate modeling of intensity variations thus reducing false positives. Moreover only scans with normal tissues are required to create the model. Twelve normal scans were used to generate the LIM and validation was conducted on a set of 46 scans. Similarity indices between the proposed approach and manual segmentations were 0.59±0.15, 0.65±0.08 and 0.77±0.08 for subjects with small, moderate and large volume of lesions respectively. The proposed approach performed better than support vector machines on the same dataset and compared favorably to approaches in literature.
Starting Page	2057
Ending Page	2060
File Size	306093
Page Count	4
File Format	PDF
ISBN	9781424441273
ISSN	19457928
DOI	10.1109/ISBI.2011.5872817
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-03-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lesions Support vector machines Image segmentation Manuals Silicon Training Magnetic resonance imaging outlier detection White matter hyperintensities Alzheimer's disease local intensity mode segmentation
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

Detection of Alzheimer's disease through automated hippocampal segmentation

Alzheimer's detection at early stage using local measures on MRI: A comparative study on local measures

Automatic segmentation of age-related white matter changes on flair images: Method and multicentre validation

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease

Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies

Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images

Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies

A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation

White matter hyperintensity burden on magnetic resonance imaging in essential tremor.

Local intensity model: An outlier detection framework with applications to white matter hyperintensity segmentation

Similar Documents

Detection of Alzheimer's disease through automated hippocampal segmentation

Alzheimer's detection at early stage using local measures on MRI: A comparative study on local measures

Automatic segmentation of age-related white matter changes on flair images: Method and multicentre validation

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease

Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies

Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images

Characterization of White Matter Hyperintensities in Large-Scale MRI-Studies

A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation

White matter hyperintensity burden on magnetic resonance imaging in essential tremor.

Local intensity model: An outlier detection framework with applications to white matter hyperintensity segmentation