25 research outputs found
A Region-based Randers Geodesic Approach for Image Segmentation
The minimal path model based on the Eikonal partial differential equation has
served as a fundamental tool for the applications of image segmentation and
boundary detection in the passed two decades. However, the existing approaches
commonly only exploit the image edge-based features for computing minimal
paths, potentially limiting their performance in complicated segmentation
situations. In this paper, we introduce a new variational image segmentation
model based on the minimal path framework and the eikonal PDE, where the
region-based appearance term that defines then regional homogeneity features
can be taken into account for estimating the associated minimal paths. This is
done by constructing a Randers geodesic metric interpretation to the
region-based active contour energy. As a result, the minimization of the active
contour energy is transformed to finding the solution to the Randers eikonal
PDE.
We also suggest a practical interactive image segmentation strategy, where
the target boundary can be delineated by the concatenation of the piecewise
geodesic paths. We invoke the Finsler variant of the fast marching method to
estimate the geodesic distance map, yielding an efficient implementation of the
proposed Eikonal region-based active contour model. Experimental results on
both synthetic and real images exhibit that our model indeed achieves
encouraging segmentation performance
Deformable Multisurface Segmentation of the Spine for Orthopedic Surgery Planning and Simulation
Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data.
Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation.
Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit.
Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung