• Publications
  • Influence
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima.
Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm
The authors propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations.
SUSAN—A New Approach to Low Level Image Processing
This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction and the resulting methods are accurate, noise resistant and fast.
Temporal Autocorrelation in Univariate Linear Modeling of FMRI Data
Estimation is improved by using nonlinear spatial filtering to smooth the estimated autocorrelation, but only within tissue type, and reduced bias to close to zero at probability levels as low as 1 x 10(-5).
Saliency, Scale and Image Description
  • T. Kadir, M. Brady
  • Computer Science
    International Journal of Computer Vision
  • 1 November 2001
A multiscale algorithm for the selection of salient regions of an image is introduced and its application to matching type problems such as tracking, object recognition and image retrieval is demonstrated.
Estimating the bias field of MR images
The authors show that replacing the class other, which includes all tissue not modeled explicitly by Gaussians with small variance, by a uniform probability density, and amending the expectation-maximization (EM) algorithm appropriately, gives significantly better results.
MRI-Based Attenuation Correction for PET/MRI: A Novel Approach Combining Pattern Recognition and Atlas Registration
A combination of local pattern recognition and atlas registration is used to predict pseudo-CT images from a given MR image, which allows reliable MRI-based attenuation correction for human brain scans and enables PET quantification with a mean error of 3.2%.