NDLI: Segmenting Brain MRI using Adaptive Mean Shift

Content Provider	IEEE Xplore Digital Library
Author	Jimenez-Alaniz, R.J. Pohi-Alfaro, M. Medina-Bafluelos, V. Yaflez-Suarez, O.
Copyright Year	2006
Description	Author affiliation: Dept. of Electr. Eng., Univ. Autonoma Metropolitana-Iztapalapa, Mexico City (Jimenez-Alaniz, R.J.; Pohi-Alfaro, M.; Medina-Bafluelos, V.; Yaflez-Suarez, O.)
Abstract	To delineate arbitrarily shaped clusters in a complex multimodal feature space, such as the brain MRI intensity space, often requires kernel estimation techniques with locally adaptive bandwidths, such as the adaptive mean shift procedure. Proper selection of the kernel bandwidth is a critical step for a better quality in the clustering. This paper presents a solution for the bandwidth selection, which is completely nonparametric and is based on the sample point estimator to yield a spatial pattern of local bandwidths. The method was applied to synthetic brain images, showing a high performance even in the presence of varying noise level and bias
Sponsorship	IEEE EMB
Starting Page	3114
Ending Page	3117
File Size	275376
Page Count	4
File Format	PDF
ISBN	1424400325
ISSN	1557170X
DOI	10.1109/IEMBS.2006.260480
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Bandwidth Kernel Image segmentation Parametric statistics Reactive power Cities and towns USA Councils Yield estimation Brain
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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

Brain anatomical structure segmentation by adaptive bandwidth density estimation

Brain Tissue Mapping and Segmentation by MRI-based Blind-Source-Separation Techniques

Gene selection for Brain Cancer Classification

The brain mapping on reinforcement acupuncture stimulation at ST36 (zusanli) evidenced by fMRI

Segmentation of brain MRI by adaptive mean shift

Evaluation of Two Segmentation Methods on MRI Brain Tissue Structures

Smooth bilinear classification of EEG

Robust Nonparametric Segmentation of Infarct Lesion from Diffusion-Weighted MR Images

Frequency-selective quantification of short-echo time Magnetic Resonance spectra

Segmenting Brain MRI using Adaptive Mean Shift

Similar Documents

Brain anatomical structure segmentation by adaptive bandwidth density estimation

Brain Tissue Mapping and Segmentation by MRI-based Blind-Source-Separation Techniques

Gene selection for Brain Cancer Classification

The brain mapping on reinforcement acupuncture stimulation at ST36 (zusanli) evidenced by fMRI

Segmentation of brain MRI by adaptive mean shift

Evaluation of Two Segmentation Methods on MRI Brain Tissue Structures

Smooth bilinear classification of EEG

Robust Nonparametric Segmentation of Infarct Lesion from Diffusion-Weighted MR Images

Frequency-selective quantification of short-echo time Magnetic Resonance spectra

Segmenting Brain MRI using Adaptive Mean Shift