Abstract
BackgroundDynamical 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.MethodologyFirst 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.ConclusionsWe conclude by showing the utility of these biomimetic methods with some examples of application in medical imaging and computed assisted surgery.
Highlights
Dynamical systems like neural networks based on lateral inhibition have a large field of applications in image processing, robotics and morphogenesis modeling
That requires a rigorous mathematical framework for defining the continuous flow and its convergence speed to attractors, and after its discrete version, i.e. an iteration process representing the succession of states of the dynamical system. These theoretical advances have permitted the development of fast image processing algorithms used in rapid contrasting methods [45,46,47,48,49,50,51,52,53,54,55,56,57,58] implemented in realtime processors [59,60,61,62,63,64,65,66,67,68], and the development of contouring methods like snakes, snake-splines, d-snakes, which allow a global definition of the boundaries of objects of interest in an image
Using the previously introduced theoretical notions, we study an enhancement method for contrasting medical images, using either a discrete neural network approach, or its continuous version, i.e. a reaction-diffusion partial differential system [92,93,94,95,96,97,98,99]
Summary
‘‘In nova fert animus mutatas dicere formas corpora...’’ I want to speak about bodies changed into new forms... (Ovid, Metamorphoses, Book 1st, 10 AD). That requires a rigorous mathematical framework for defining the continuous flow and its convergence speed to attractors, and after its discrete version, i.e. an iteration process representing the succession of states of the dynamical system These theoretical advances have permitted the development of fast image processing algorithms used in rapid contrasting methods [45,46,47,48,49,50,51,52,53,54,55,56,57,58] implemented in realtime processors [59,60,61,62,63,64,65,66,67,68], and the development of contouring methods like snakes, snake-splines, d-snakes, which allow a global definition of the boundaries of objects of interest in an image. We introduce continuous operators generalizing discrete neuromimetic approaches using lateral inhibition as well as analogs of the Hebbian rule for the evolution of synaptic weights
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