This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory

This paper describes a new international collaborative initiative for the assembly of a high level medical image analysis toolkit, based on Mathematica, to do efficient design and development of advanced computer vision algorithms for computer-aided diagnosis. Mathematica has come to a point

of this dissertation is medical image analysis with focus on multi-scale methods and the task of segmentation. The main theme is how to move from explicitly non-committed

Grids have emerged as a promising technology to handle the data and compute intensive requirements of many application areas. Digital medical image processing is a promising application area for grids. Given the volume of data, the sensitivity of medical information, and the joint complexity

Functional magnetic resonance imaging (fMRI) is a prime example of multidisciplinary research. Without the beautiful physics of MRI, there would not be any images to look at in the first place. To obtain images of good quality, it is necessary to fully understand the concepts of the frequency

Abstract. We derive a new model for simultaneous segmentation and deconvolution of dynamic PET images. By incorporating the PSF of the imaging system into our segmentation model, we simultaneously estimate region boundaries, and correct tissue activities for the partial volume effect. We show

-up structural brain imaging studies of patients with schizophrenia treated with antipsychotics. 30 magnetic resonance imaging (MRI) studies were identified, 24 of them being longitudinal and six cross-sectional structural imaging studies. In patients with schizophrenia treated with antipsychotics, reduced gray

Abstract. We propose a semi-supervised, kinetic modeling based segmentation technique for molecular imaging applications. It is an iterative, self-learning algorithm based on uncertainty principles, designed to alleviate low signal-to-noise ratio (SNR) and partial volume effect (PVE) problems

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: