Summer School and Workshop on
Imaging Sciences and
Medical Applications
  June 15-23, 2010  
DM-FCTUC, University of Coimbra
Coimbra, Portugal
Medical Images: DM-FCTUC, FMUC and ICNAS - University of Coimbra
  Untitled Document
Summer School courses (June 15-19, 2010)

Highly accurate image restoration and matching
Andres Almansa (Telecom Paris Tech, France)
     Image sampling (hexagonal and irregular) restoration (of bandlimited blurred and noisy images from those samples), and reliable sub-pixel block-matching will be addressed. Techniques involved include harmonic and non-harmonic analysis, TV minimization, and a particular kind of statistical hypothesis testing called "a contrario" methods.


Variational models in image inpainting
Selim Esedoglu (University of Michigan, USA)
     Image inpainting is the process of automatically filling in damaged regions in digitized pictures with information gleaned from surrounding, undamaged areas. It has been a very active area of mathematical research in image processing. We will describe some of the variational and partial differential equations based models proposed for this application, and discuss efficient numerical methods for their solution. Topics will include some of the more recent non-local models.


Image segmentation
Sung Ha Kang (Georgia Institute of Technology, Atlanta, USA)

      An important problem in image processing and computer vision is the segmentation one, which aims to find boundaries of objects in images or to partition a given image into its constituent objects. One of the main applications of segmentation is in the medical field. In this course, deterministic approaches for image segmentation and active contours will be presented, using variational formulations, nonlinear partial differential equations and level sets. Most relevant edge-based and region-based models will be described in details, together with their extensions to color, texture, or medical images. Numerical algorithms will be presented in details.


Image reconstruction in tomography
Alfred K. Louis (Saarland University, Germany)
     In imaging technologies, both in medicine and in non-destructive testing, the task is to reconstruct the desired information from measured data. In a first step, the forward problem has to be addressed, namely the development of mathematical models. The reconstruction then is the inverse problem. We study several imaging technologies as x-ray CT, MRI and ultrasound CT.
     As model case we consider the Radon transform as mathematical model for 2D CT. We derive inversion formulae and analyze the principles for constructing fast algorithms. We also consider question of uniqueness and resolution for a given data set. Finally we include the data analysis part into the reconstruction in order to determine features of the image in just one step. We discuss optimal filters and study the behavior for real data sets.
     We discuss extensions of the methods to 3D X-ray CT and ultrasound CT.


Flexible algorithms for image registration
Jan Modersitzki (McMaster University, Canada)
     A generic task in modern image processing is image registration, needed for integration and/or comparison of data obtained from different images. Particularly in a medical environment, there is a huge demand for comparing pre- and post-intervention images, integrating modalities like anatomy (obtained, e.g., from computer tomography) and functionality (obtained, e.g., from positron emission tomography), motion correction and/or reconstruction of two-dimensional projections from a three-dimensional volume (applies to all tomography techniques and histology). The problem is easily stated: given two images (a reference and a template image), and a transformation, such that the transformed image is similar to the reference image.
     In this course we present a general and unified approach to image registration. The course covers central problems arising in typical applications. The course covers theoretical as well as practical components. Implementation issues are discussed on the basis of the FAIR software, see for details.


austin   cim   cmuc   rnif   sped
    Web Page developed by David Pardal (AEISCAC, Coimbra)