Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wavelets and subband coding
Introduction to statistical signal processing with applications
Introduction to statistical signal processing with applications
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Restriction of a Markov random field on a graph and multiresolution statistical image modeling
IEEE Transactions on Information Theory
Multiscale segmentation and anomaly enhancement of SAR imagery
IEEE Transactions on Image Processing
Wavelet-based image denoising using a Markov random field a priori model
IEEE Transactions on Image Processing
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This paper considers detection of functional magnetic resonance images (fMRIs), that is, to decide active and nonactive regions of human brain from fMRIs. A novel two-step approach is put forward that incorporates spatial correlation information and is amenable to analysis and optimization. First, a new multi-scale image segmentation algorithm is proposed to decompose the correlation image into several different regions, each of which is of homogeneous statistical behavior. Second, each region will be classified independently as active or inactive using existing pixel-wise test methods. The image segmentation consists of two procedures: edge detection followed by label estimation. To deduce the presence or absence of an edge from continuous data, two fundamental assumptions of our algorithm are 1) each wavelet coefficient is described by a 2-state Gaussian Mixture Model (GMM); 2) across scales, each state is caused by its parent state, hence the name of multiscale hidden Markov model (MHMM). The states of Markov chain are unknown ("hidden") and represent the presence (state 1) or absence (state 0) of edges. Using this interpretation, the edge detection problem boils down to the posterior state estimation given obervation.