538,928 research outputs found
Multi-scale active shape description in medical imaging
Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed
Statistical Model of Shape Moments with Active Contour Evolution for Shape Detection and Segmentation
This paper describes a novel method for shape representation and robust image segmentation. The proposed method combines two well known methodologies, namely, statistical shape models and active contours implemented in level set framework. The shape detection is achieved by maximizing a posterior function that consists of a prior shape probability model and image likelihood function conditioned on shapes. The statistical shape model is built as a result of a learning process based on nonparametric probability estimation in a PCA reduced feature space formed by the Legendre moments of training silhouette images. A greedy strategy is applied to optimize the proposed cost function by iteratively evolving an implicit active contour in the image space and subsequent constrained optimization of the evolved shape in the reduced shape feature space. Experimental results presented in the paper demonstrate that the proposed method, contrary to many other active contour segmentation methods, is highly resilient to severe random and structural noise that could be present in the data
Soft inclusion in a confined fluctuating active gel
We study stochastic dynamics of a point and extended inclusion within a one
dimensional confined active viscoelastic gel. We show that the dynamics of a
point inclusion can be described by a Langevin equation with a confining
potential and multiplicative noise. Using a systematic adiabatic elimination
over the fast variables, we arrive at an overdamped equation with a proper
definition of the multiplicative noise. To highlight various features and to
appeal to different biological contexts, we treat the inclusion in turn as a
rigid extended element, an elastic element and a viscoelastic (Kelvin-Voigt)
element. The dynamics for the shape and position of the extended inclusion can
be described by coupled Langevin equations. Deriving exact expressions for the
corresponding steady state probability distributions, we find that the active
noise induces an attraction to the edges of the confining domain. In the
presence of a competing centering force, we find that the shape of the
probability distribution exhibits a sharp transition upon varying the amplitude
of the active noise. Our results could help understanding the positioning and
deformability of biological inclusions, eg. organelles in cells, or nucleus and
cells within tissues.Comment: 16 pages, 9 figure
Colloquium: Mechanical formalisms for tissue dynamics
The understanding of morphogenesis in living organisms has been renewed by
tremendous progressin experimental techniques that provide access to
cell-scale, quantitative information both on theshapes of cells within tissues
and on the genes being expressed. This information suggests that
ourunderstanding of the respective contributions of gene expression and
mechanics, and of their crucialentanglement, will soon leap forward.
Biomechanics increasingly benefits from models, which assistthe design and
interpretation of experiments, point out the main ingredients and assumptions,
andultimately lead to predictions. The newly accessible local information thus
calls for a reflectionon how to select suitable classes of mechanical models.
We review both mechanical ingredientssuggested by the current knowledge of
tissue behaviour, and modelling methods that can helpgenerate a rheological
diagram or a constitutive equation. We distinguish cell scale ("intra-cell")and
tissue scale ("inter-cell") contributions. We recall the mathematical framework
developpedfor continuum materials and explain how to transform a constitutive
equation into a set of partialdifferential equations amenable to numerical
resolution. We show that when plastic behaviour isrelevant, the dissipation
function formalism appears appropriate to generate constitutive equations;its
variational nature facilitates numerical implementation, and we discuss
adaptations needed in thecase of large deformations. The present article
gathers theoretical methods that can readily enhancethe significance of the
data to be extracted from recent or future high throughput
biomechanicalexperiments.Comment: 33 pages, 20 figures. This version (26 Sept. 2015) contains a few
corrections to the published version, all in Appendix D.2 devoted to large
deformation
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