Understanding Physiological and Degenerative Natural Vision Mechanisms to Define Contrast and Contour Operators

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.

Social concerns of ubiquitous computing

“…The capabilities of modelling serve goals like detecting early signs of dementia [29] [30] preventing bedsores or ocular complication or degeneration [31]. These goals can be seen as enhancing functional autonomy of patients.…”

Robustness in Regulatory Interaction Networks. A Generic Approach with Applications at Different Levels: Physiologic, Metabolic and Genetic

“…The applications of the n -switches concern the morphogenesis of the brain in which a 2-switch (made of transduction peptides in [ 89 ]) controls the interactions between early brain cells [ Figure 12(a) left], the double inhibition leading to a spatial segregation between cell populations [ Figure 12(a) middle] which differentiate in a second stage [ Figure 12(a) right] giving for example separated (rods and cones in retina [ 90 ]) or intricate (neurons and astrocytes) complementary tissues. Many factors that control cells fate are themselves targets of such inhibitory proteins: neural induction in vertebrates [ 91 , 92 ] as well as plant growth [ 88 ] with inhibition between meristem and buds may be mediated through such a mechanism [ Figure 12(c) right] very sensitive to the initial conditions; each attractor corresponds to the local dominance of a node of the n -switch over the others [ Figure 12(b) left].…”

Synchrony in reaction–diffusion models of morphogenesis: applications to curvature-dependent proliferation and zero-diffusion front waves

“…Feather morphogenesis with identification of an activator (BMP-7), an inhibitor (BMP-2) (followed by chemical staining in (a(i)) and a close-up view of (a(i)) in (a(ii))) and a mediator (follistatine) (followed by chemical staining in (b(i)) and a close-up view of (b(i)) in (b(ii))) as morphogenes interacting at the genetic level (c), for giving first feather primordia (d(i)) and after adult feathers allowing the wheel of feathers in the peacock (d(ii)). Another very important parameter is the ratio between the diffusion coefficients D u /D v , which is less than 1, as is usual in lateral inhibition (Demongeot et al 2009): if the ratio is equal to the critical value 0.06, we observe both in experiments (figure 4) and in simulations (figure 5) a spatiotemporal synchrony between the effectors u and v; in particular, experiments and simulations show a coincidence of their remarkable Gaussian lines, i.e. the projections of the null-curvature lines on the u and v concentration surfaces, verifying…”