NDLI: Efficient level-set segmentation model driven by the local GMM and split Bregman method

Content Provider	IET Digital Library
Author	Wang, Dengwei
Abstract	An efficient level-set model driven by the local Gaussian mixture model (GMM) and split Bregman method is proposed for image segmentation. Firstly, the two intensity fitting functions in the original local binary fitting (LBF) model are pre-estimated by using a local GMM-based intensity distribution estimator before curve evolution. The benefit of this pre-computation strategy is to avoid updating the fitting functions at each step of the curve evolution, and it also overcomes the initialisation problem of common gradient descent-based active contour models, i.e. the level-set function can be initialised as an arbitrary random matrix instead of a signed distance function in the proposed processing framework. Secondly, two processing ideas named global convex segmentation (GCS) method and split Bregman are introduced into the numerical implementation, where the role of GCS is to transform the proposed model into a simplified convex segmentation model, and the purpose of the split Bregman is to quickly output a convergent solution of the newly derived convex segmentation model in an alternate iterative format. Experimental results for synthetic and real images of different modalities with inhomogeneity or homogeneity validate the desired performances of the proposed method in terms of accuracy, robustness, and rapidity.
Starting Page	761
Ending Page	770
Page Count	10
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 5, Apr (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6216
Journal	IET Image Processing
Publisher Date	2019-01-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Common Gradient Descent-based Active Contour Model Computer Vision And Image Processing Technique Curve Evolution Efficient Level-set Model Efficient Level-set Segmentation Model Gaussian Processes Global Convex Optimisation Global Convex Segmentation Method Gradient Method Image Segmentation Intensity Fitting Function Iterative Method Level-set Function Level-set Model Shows Excellent Processing Performance Local Gaussian Mixture Model Local GMM-based Intensity Distribution Estimator Medical Image Processing Modified LBF Model Newly Derived Convex Segmentation Model Optical, Image And Video Signal Processing Optimisation Technique Original Local Binary Fitting Model Patient Diagnostic Method And Instrumentation Pre-computation Strategy Segmentation Application Signed Distance Function Simplified Convex Segmentation Model Split Bregman Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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

Fast image segmentation by convex minimisation and split Bregman method

A note on convex image segmentation model based on local and global intensity fitting energy

Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

Convex-relaxed active contour model based on localised kernel mapping

Image Segmentation Model Based on Local Image Fitting Energy and Split Bregman Method

Level set method for image segmentation based on local variance and improved intensity inhomogeneity model

Hybrid fitting energy-based fast level set model for image segmentation solving by algebraic multigrid and sparse field method

Fast algorithm for hybrid region-based active contours optimisation

Heat diffusion embedded level set evolution for infrared image segmentation

Efficient level-set segmentation model driven by the local GMM and split Bregman method

Similar Documents

Fast image segmentation by convex minimisation and split Bregman method

A note on convex image segmentation model based on local and global intensity fitting energy

Convex Image Segmentation Model Based on Local and Global Intensity Fitting Energy and Split Bregman Method

Convex-relaxed active contour model based on localised kernel mapping

Image Segmentation Model Based on Local Image Fitting Energy and Split Bregman Method

Level set method for image segmentation based on local variance and improved intensity inhomogeneity model

Hybrid fitting energy-based fast level set model for image segmentation solving by algebraic multigrid and sparse field method

Fast algorithm for hybrid region-based active contours optimisation

Heat diffusion embedded level set evolution for infrared image segmentation

Efficient level-set segmentation model driven by the local GMM and split Bregman method