1,895 research outputs found
Gaussian Process Morphable Models
Statistical shape models (SSMs) represent a class of shapes as a normal
distribution of point variations, whose parameters are estimated from example
shapes. Principal component analysis (PCA) is applied to obtain a
low-dimensional representation of the shape variation in terms of the leading
principal components. In this paper, we propose a generalization of SSMs,
called Gaussian Process Morphable Models (GPMMs). We model the shape variations
with a Gaussian process, which we represent using the leading components of its
Karhunen-Loeve expansion. To compute the expansion, we make use of an
approximation scheme based on the Nystrom method. The resulting model can be
seen as a continuous analogon of an SSM. However, while for SSMs the shape
variation is restricted to the span of the example data, with GPMMs we can
define the shape variation using any Gaussian process. For example, we can
build shape models that correspond to classical spline models, and thus do not
require any example data. Furthermore, Gaussian processes make it possible to
combine different models. For example, an SSM can be extended with a spline
model, to obtain a model that incorporates learned shape characteristics, but
is flexible enough to explain shapes that cannot be represented by the SSM. We
introduce a simple algorithm for fitting a GPMM to a surface or image. This
results in a non-rigid registration approach, whose regularization properties
are defined by a GPMM. We show how we can obtain different registration
schemes,including methods for multi-scale, spatially-varying or hybrid
registration, by constructing an appropriate GPMM. As our approach strictly
separates modelling from the fitting process, this is all achieved without
changes to the fitting algorithm. We show the applicability and versatility of
GPMMs on a clinical use case, where the goal is the model-based segmentation of
3D forearm images
Optimization for automated assembly of puzzles
The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is formulated as an optimization problem where the optimum assembly of the pieces is achieved by maximizing the total affinity measure. An fft based image registration technique is used to speed up the alignment of the pieces. Experimental results are presented on real and artificial data sets
2D Reconstruction of Small Intestine's Interior Wall
Examining and interpreting of a large number of wireless endoscopic images
from the gastrointestinal tract is a tiresome task for physicians. A practical
solution is to automatically construct a two dimensional representation of the
gastrointestinal tract for easy inspection. However, little has been done on
wireless endoscopic image stitching, let alone systematic investigation. The
proposed new wireless endoscopic image stitching method consists of two main
steps to improve the accuracy and efficiency of image registration. First, the
keypoints are extracted by Principle Component Analysis and Scale Invariant
Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood
Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable
keypoints. Second, the optimal transformation parameters obtained from first
step are fed to the Normalised Mutual Information (NMI) algorithm as an initial
solution. With modified Marquardt-Levenberg search strategy in a multiscale
framework, the NMI can find the optimal transformation parameters in the
shortest time. The proposed methodology has been tested on two different
datasets - one with real wireless endoscopic images and another with images
obtained from Micro-Ball (a new wireless cubic endoscopy system with six image
sensors). The results have demonstrated the accuracy and robustness of the
proposed methodology both visually and quantitatively.Comment: Journal draf
Proceedings of the FEniCS Conference 2017
Proceedings of the FEniCS Conference 2017 that took place 12-14 June 2017 at the University of Luxembourg, Luxembourg
Multimodal T2w and DWI Prostate Gland Automated Registration
Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre-and post-registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI
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