The analysis of medical images has been woven into the fabric of the pattern analysis and machine intelligence (PAMI) community since the earliest days of these Transactions. Initially, the efforts in this area were seen as applying pattern analysis and computer vision techniques to another interesting dataset. However, over the last two to three decades, the unique nature of the problems presented within this area of study have led to the development of a new discipline in its own right. Examples of these include: the types of image information that are acquired, the fully three-dimensional image data, the nonrigid nature of object motion and deformation, and the statistical variation of both the underlying normal and abnormal ground truth. In this paper, we look at progress in the field over the last 20 years and suggest some of the challenges that remain for the years to come.

https://hal.inria.fr/inria-00615100/document

Medical image analysis: progress over two decades and the challenges ahead

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.

/pdf/a-pyramid-approach-to-subpixel-registration-based-on-41xhw0es6x.pdf

A pyramid approach to subpixel registration based on intensity

https://hal.inria.fr/inria-00615088/document

Image matching as a diffusion process: an analogy with Maxwell's demons

1. Introduction 2. The Human Neuroscanning Project 3. The mathematical setting I PARAMETRIC IMAGE REGISTRATION 4. Landmark based registration 5. Principal axes based registration 6. Optimal linear registration 7. Summarizing parametric image registration II NON-PARAMETRIC IMAGE REGISTRATION 8. Non-parametric image registration 9. Elastic registration 10. Fluid registration 11. Diffusion registration 12. Curvature registration 13. Concluding remarks

Numerical Methods for Image Registration

A stereotactic navigation system for navigating an instrument to a target within a patient may include a stereotactic head frame, an imaging device, a tracking device, a controller and a display. The stereotactic head frame is coupled to the patient and is used to assist in guiding the instrument to the target. The imaging device captures image data of the patient and of the stereotactic head frame. The tracking device is used to track the position of the instrument relative to the stereotactic head frame. The controller receives the image data from the imaging device and identifies the stereotactic head frame in the image data and automatically registers the image data with navigable patient space upon identifying the stereotactic head frame, while the display displays the image data.

Method and apparatus for performing stereotactic surgery

An apparatus and method for image registration involves computing (350) a first transform based on landmark manifold points (102, 104, 114), using a distance measure, computing (370) a second transform from the distance measure and the first transform. Registering the images is accomplished by applying the second transform.

Method and apparatus for image registration

Proposes a framework for simultaneous detection, tracking, and recognition of objects via data fused from multiple sensors. Complex dynamic scenes are represented via the concatenation of simple rigid templates. The variability of the infinity of pose is accommodated via the actions of matrix Lie groups extending the templates to individual instances. The variability of target number and target identity is accommodated via the representation of scenes as unions of templates of varying types, with the associated group transformations of varying dimension. We focus on recognition in the air-to-ground and ground-to-air scenarios. The remote sensing data is organized around both the coarse scale associated with detection as provided by tracking and range radars, along with the fine scale associated with pose and identity supported by high-resolution optical, forward looking infrared and delay-Doppler radar imagers. A Bayesian approach is adopted in which prior distributions on target scenarios are constructed via dynamical models of the targets of interest. These are combined with physics-based sensor models which define conditional likelihoods for the coarse/fine scale sensor data given the underlying scene. Inference via the Bayes posterior is organized around a random sampling algorithm based on jump-diffusion processes. New objects are detected and object identities are recognized through discrete jump moves through parameter space, the algorithm exploring scenes of varying complexity as it proceeds. Between jumps, the scale and rotation group transformations are generated via continuous diffusions in order to smoothly deform templates into individual instances of objects.

Automatic target recognition organized via jump-diffusion algorithms

A method and apparatus for classifying population states based on shape characterizations of sub-manifolds of points, curves, surfaces, or sub-volumes. A structure is examined using, for example, clinical imaging techniques such as CT, MRI, or Ultrasound (601). The image is then subjected to a transform function to generate a map (602) of the new image. The new image map which contains information regarding the shape (604) of the structure is compared (606) to average shape characterizing population groups. If the shape of the new image falls within a best match probability with an average shape, the new image is classified (607) as a member of the population characterized by the average shape. Thus, if the shape of the new structure resembles the average shape of a population group, the new shape is classified as the same population state as the other structures displaying the same shape characteristics.

Method and apparatus for automatic shape characterization

A new language model incorporating both N-gram and context-free ideas is proposed. This constrained context-free model is specified by a stochastic context-free prior distribution with N-gram frequency constraints. The resulting distribution is a Markov random field. Algorithms for sampling from this distribution and estimating the parameters of the model are presented.

Parameter estimation for constrained context-free language models

izing Neuro- assively

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