Unsupervised vector image segmentation by a tree structure-ICM algorithm
IEEE Transactions on Medical ImagingPublished 1 December 1996
Jong-Kae Fwu, Petar M. Djurić
Citations47
SJR quartileQ1
SJR score2.63
SNIP3.00
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A new maximum a posteriori (MAP) criterion for determination of the number of classes is proposed and its performance is compared to other approaches by computer simulations.
Abstract
In recent years, many image segmentation approaches have been based on Markov random fields (MRFs). The main assumption of the MRF approaches is that the class parameters are known or can be obtained from training data. In this paper the authors propose a novel method that relaxes this assumption and allows for simultaneous parameter estimation and vector image segmentation. The method is based on a tree structure (TS) algorithm which is combined with Besag's iterated conditional modes (ICM) procedure. The TS algorithm provides a mechanism for choosing initial cluster centers needed for initialization of the ICM. The authors' method has been tested on various one-dimensional (1-D) and multidimensional medical images and shows excellent performance. In this paper the authors also address the problem of cluster validation. They propose a new maximum a posteriori (MAP) criterion for determination of the number of classes and compare its performance to other approaches by computer simulations.
Keywords
Computer Science
IEEE Transactions on Automatic ControlA new look at the statistical model identification
50,732 Citations1974Hirotugu Akaike
Journal of the Royal Statistical Society Series B (Statistical Methodology)Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
49,657 Citations1977A. P. Dempster, N. M. Laird +1 more
IEEE Transactions on Pattern Analysis and Machine IntelligenceStochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
17,980 Citations1984Stuart Geman, Donald Geman
The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Vector Quantization and Signal Compression
7,039 Citations1992A. Gersho, Robert M. Gray
The author explains the design and implementation of the Levinson-Durbin Algorithm, which automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing a Quantizer.
Journal of the Royal Statistical Society Series B (Statistical Methodology)Spatial Interaction and the Statistical Analysis of Lattice Systems
6,179 Citations1974Julian Besag
AutomaticaModeling by shortest data description
5,944 Citations1978J. Rissanen
The number of digits it takes to write down an observed sequence x1,...,xN of a time series depends on the model with its parameters that one assumes to have generated the observed data.
Digital Access to Scholarship at Harvard (DASH) (Harvard University)Maximum Likelihood from Incomplete Data via the EM Algorithm
4,523 Citations1977Dempster, Arthur P., Laird, Nan M. +1 more
Journal of the Royal Statistical Society Series B (Statistical Methodology)On the Statistical Analysis of Dirty Pictures
4,228 Citations1986Julian Besag
A simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics of the random field, and is illustrated by computer simulations in which the original scene is not directly related to the assumed random field.
BiometricsModel-Based Gaussian and Non-Gaussian Clustering
2,350 Citations1993Jeffrey D. Banfield, Adrian E. Raftery
The classification maximum likelihood approach is sufficiently general to encompass many current clustering algorithms, including those based on the sum of squares criterion and on the criterion of Friedman and Rubin (1967), but it is restricted to Gaussian distributions and it does not allow for noise.
Journal of the American Statistical AssociationBayes Factors
2,303 Citations1995Robert E. Kass, Adrian E. Raftery
Encyclopedia of Statistics in Behavioral ScienceFinite Mixture Distributions
1,287 Citations2005Barry J. Everitt
IEEE Transactions on Image ProcessingA new criterion for automatic multilevel thresholding
932 Citations1995Jui-Cheng Yen, Fu-Juay Chang +1 more
A new criterion for multilevel thresholding based on the consideration of two factors, the discrepancy between thresholded and original images and the number of bits required to represent the thresholded image, and a cost function that takes both factors into account are proposed.
Systems Research and Behavioral ScienceA clustering technique for summarizing multivariate data
908 Citations1967Geoffrey H. Ball, D.J. Hall
A practical computing method termed ISODATA, which finds the cluster structure of such data, is described and provides a fit to the data of a set of cluster centers that tends to minimize the sum of the squared distances of each data point from its closest cluster center.
Journal of the American Statistical AssociationProbabilistic Solution of Ill-Posed Problems in Computational Vision
698 Citations1987José L. Marroquín, Sanjoy K. Mitter +1 more
This work derives efficient algorithms and describes parallel implementations on digital parallel SIMD architectures, as well as a new class of parallel hybrid computers that mix digital with analog components.
IEEE Transactions on Image ProcessingA multiscale random field model for Bayesian image segmentation
599 Citations1994Charles A. Bouman, M. V. Shapiro
Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing, and is found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.
IEEE Transactions on Signal ProcessingAn adaptive clustering algorithm for image segmentation
586 Citations1992Thrasyvoulos N. Pappas
International Journal of Computer VisionConstructing simple stable descriptions for image partitioning
517 Citations1989Yvan G. Leclerc
A new formulation of the image partitioning problem is presented: construct a complete and stable description of an image-in terms of a specified descriptive language-that is simplest in the sense of being shortest, which yields intuitively satisfying partitions for a wide class of images.
Proceedings of the IEEEOrder statistics in digital image processing
477 Citations1992Ioannis Pitas, A.N. Venetsanopoulos
A family of nonlinear filters based on order statistics is presented and the probabilistic and deterministic properties of the best known and most widely used filter in this family, the median filter, are discussed.
IEEE Transactions on Pattern Analysis and Machine IntelligenceParallel and deterministic algorithms from MRFs: surface reconstruction
449 Citations1991Davi Geiger, Federico Girosi
Deterministic approximations to Markov random field (MRF) models are derived and one of the models is shown to give in a natural way the graduated nonconvexity (GNC) algorithm proposed by A. Blake and A. Zisserman (1987).
Journal of the American Statistical AssociationProbabilistic Solution of Ill-Posed Problems in Computational Vision
367 Citations1987José L. Marroquín, Sanjoy K. Mitter +1 more
IEEE Transactions on Pattern Analysis and Machine IntelligenceSimultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing
357 Citations1989Sridhar Lakshmanan, Haluk Derin
It is proven that the parameter estimates and the segmentations converge in distribution to the ML estimate of the parameters and the MAP segmentation with those parameter estimates, respectively.
IEEE Transactions on Pattern Analysis and Machine IntelligenceMultiple resolution segmentation of textured images
351 Citations1991Charles A. Bouman, B. Liu
A multiple resolution algorithm is presented for segmenting images into regions with differing statistical behavior and an algorithm is developed for determining the number of statistically distinct regions in an image and estimating the parameters of those regions.
IEEE Transactions on Signal ProcessingThe mean field theory in EM procedures for Markov random fields
340 Citations1992J. Zhang
The efficacy of the mean field theory approach is demonstrated on parameter estimation for one-dimensional mixture data and two-dimensional unsupervised stochastic model-based image segmentation and on parameter estimates for both synthetic and real-world images.
IEEE Transactions on Signal ProcessingCompound Gauss-Markov random fields for image estimation
182 Citations1991Fure-Ching Jeng, John W. Woods
The authors consider both the maximum a posteriori probability (MAP) estimate and the minimum mean-squared error (MMSE) estimate for image estimation and image restoration for images modeled as compound Gauss-Markov random fields.
IEEE Transactions on Geoscience and Remote SensingSEM algorithm and unsupervised statistical segmentation of satellite images
172 Citations1993Puneet Masson, Wojciech Pieczynski
The work addresses Bayesian unsupervised satellite image segmentation, using contextual methods, and shows that the spatial or spectral context contribution is sensitive to image parameters such as homogeneity, means, variances, andatial or spectral correlations of the noise.
IEEE Transactions on Circuits and Systems for Video TechnologyMultivariate ordering in color image filtering
162 Citations1991Ioannis Pitas, Panagiotis Tsakalides
A family of multichannel filters based on multivariate data ordering, such as the marginal Median, the vector median, the marginal alpha -trimmed mean, and the multich channel modified trimmed mean filter, is described in detail.
CVGIP Image UnderstandingMultiscale Minimization of Global Energy Functions in Some Visual Recovery Problems
156 Citations1994Fabrice Heitz, Patrick Pérez +1 more
A new comprehensive approach to minimize global energy functions using a multiscale relaxation algorithm that appears to be far less sensitive to local minima than standard relaxation algorithms is investigated.
CVGIP Graphical Models and Image ProcessingUnsupervised segmentation of noisy and textured images using Markov random fields
155 Citations1992Chee Sun Won, Haluk Derin
Special forms of the general unsupervised segmentation algorithm are developed for the segmentation of noisy and textured images, which yield good segmentations, accurate estimates for the parameters, and the correct number of regions.
MRF model-based algorithms for image segmentation
147 Citations2002Richard C. Dubes, Anil K. Jain +2 more
The authors empirically compare three algorithms for segmenting simple, noisy images and conclude that contextual information from MRF models improves segmentation when the number of categories and the degradation model are known and that parameters can be effectively estimated.
IEEE Transactions on Nuclear ScienceParameter estimation of finite mixtures using the EM algorithm and information criteria with application to medical image processing
138 Citations1992Zhen Liang, R.J. Jaszczak +1 more
The accuracy in determining the number of image classes using AIC and MDL is compared and the MDL criterion performed better than the AIC criterion, and a modified MDL showed further improvement.
IEEE Transactions on Image ProcessingSegmentation of polarimetric synthetic aperture radar data
132 Citations1992Eric Rignot, Rama Chellappa
IEEE Transactions on Pattern Analysis and Machine IntelligenceCombining image compression and classification using vector quantization
132 Citations1995K.L. Oehler, Robert M. Gray
A variety of examples demonstrate that the proposed method can provide classification ability close to or superior to learning VQ while simultaneously providing superior compression performance.
IEEE Transactions on Pattern Analysis and Machine IntelligenceThe integration of image segmentation maps using region and edge information
131 Citations1993Chen-Chau Chu, J.K. Aggarwal
An algorithm that integrates multiple region segmentation maps and edge maps operates independently of image sources and specific region-segmentation or edge-detection techniques and shows a strong resemblance to human-generated segmentation.
IEEE Transactions on Signal ProcessingA greedy tree growing algorithm for the design of variable rate vector quantizers (image compression)
131 Citations1991E.A. Riskin, Robert M. Gray
A technique for directly designing a variable-rate tree-structured vector quantizer by growing the tree one node at a time rather than one layer at time is presented.
IEEE Transactions on Pattern Analysis and Machine IntelligenceA model-fitting approach to cluster validation with application to stochastic model-based image segmentation
113 Citations1990J. Zhang, J.W. Modestino
Pattern RecognitionColor images' segmentation using scale space filter and markov random field
71 Citations1992Chung-Lin Huang, Tai-Yuen Cheng +1 more
A new hybrid method is presented that combines the scale space filter (SSF) and Markov random field (MRF) for color image segmentation and it is shown that the former is histogram-based segmentation, whereas the latter is neighborhood- based segmentation.
IEEE Transactions on Information TheoryA unified approach to tree-structured and multistage vector quantization for noisy channels
62 Citations1993N. Phamdo, N. Farvardin +1 more
It is demonstrated that the CM-MSVQ is fairly robust to channel mismatch, and can be regarded as a block-structured combined source-channel coding scheme that outperforms the tandem scheme in all cases that the authors have considered.
IEEE Transactions on Pattern Analysis and Machine IntelligenceAn Approximate Solution to Normal Mixture Identification with Application to Unsupervised Pattern Classification
56 Citations1981J.‐G. Postaire, Christian Vasseur
In this paper, an approach to unsupervised pattern classifiation is discussed, based on an approximation of the probability densities of each class under the assumption that the input patterns are of a normal mixture.
Unsupervised segmentation of textured color images using Markov random field models
17 Citations2002D. Panjwani, Glenn Healey
An unsupervised segmentation algorithm which uses Markov random fields for modeling color texture is presented and is successfully applied to a range of textured color images of natural scenes.
Unsupervised adaptive image segmentation
13 Citations2002Zoltán Kató, Josiane Zerubia +2 more
This paper's approach consists of a recent iterative method of estimation, called iterative conditional estimation (ICE), applied to a monogrid Markovian image segmentation model, and has been tested on synthetic and real satellite images.
Cluster validation for unsupervised stochastic model-based image segmentation
9 Citations2002David A. Langan, J.W. Modestino +1 more
This work investigates the cluster validation problem associated with the use of a previously developed unsupervised segmentation algorithm based upon the expectation-maximization (EM) algorithm and proposes a model-fitting technique in which the complete data log-likelihood functional is modeled as an exponential function in the number of classes acting, and the class estimate is related to the rise time.
IEEE International Conference on Acoustics Speech and Signal ProcessingDesign and theoretical analysis of a vector field segmentation algorithm
6 Citations1993Ian B. Kerfoot, Yoram Bresler
Several objective functions for vector field segmentation are presented and standard methods of signal detection and estimation are used to develop a theoretical performance analysis which quantitatively predicts the performance at realistic noise levels.
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE<title>Discontinuity-preserving vector smoothing on multivariate MR images via vector mean-field annealing</title>
3 Citations1992Youn-Sik Han, Wesley E. Snyder
A discontinuity-preserving vector smoothing technique is intrOdUced, which is based on current scalar Mean Field Annealing, and it is shown that this technique can be used to process multivariate images as a whole.