In surgical treatment of Parkinson’s disease, deep brain stimulation requires high-precision positioning of electrodes, needing
accurate localization and outlines of anatomical targets. Manual procedure of anatomical structures outlining on magnetic
resonance images (MRI) takes about several hours. We proposed an automated localizing procedure aiming to shorten this task
to some seconds. Different parameters were simultaneously assessed in our algorithm undertaking segmentation of anatomical
structures. Intraclass correlation coefficients (ICCs) were computed for centers of gravity coordinates of structures between
manual expert-mapped MRI and automated-mapped MRI. Tanimoto coefficients were computed accounting for pixels overlapping between
these two procedures. Although ICCs showed almost perfect concordance, TC provided further information with a quite severe
value about 35%. For both criteria, results were variable regarding each parameter in our process. With such complex results
to relate, their presentations were enhanced using visualization methods resembling that of the generalized Case View method.
[Show abstract][Hide abstract] ABSTRACT: Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modeling. In this paper, we will propose some examples of dynamical flows used in image contrasting and contouring.
First we present the physiological basis of the retina function by showing the role of the lateral inhibition in the optical illusions and pathologic processes generation. Then, based on these biological considerations about the real vision mechanisms, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a non-isotropic diffusion reaction partial differential system. Following this, we introduce other continuous operators based on similar biomimetic approaches: a chemotactic contrasting method, a viability contouring algorithm and an attentional focus operator. Then, we introduce the new notion of mixed potential Hamiltonian flows; we compare it with the watershed method and we use it for contouring.
We conclude by showing the utility of these biomimetic methods with some examples of application in medical imaging and computed assisted surgery.
PLoS ONE 02/2009; 4(6):e6010. DOI:10.1371/journal.pone.0006010 · 3.23 Impact Factor
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