Abstract
Automated analysis of structural imaging such as lung Computed Tomography (CT) plays an increasingly important role in medical imaging applications. Despite significant progress in the development of image registration and segmentation methods, lung registration and segmentation remain a challenging task. In this paper, we present a novel image registration and segmentation approach, for which we develop a new mathematical formulation to jointly segment and register three-dimensional lung CT volumes. The new algorithm is based on a level-set formulation, which merges a classic Chan–Vese segmentation with the active dense displacement field estimation. Combining registration with segmentation has two key advantages: it allows to eliminate the problem of initializing surface based segmentation methods, and to incorporate prior knowledge into the registration in a mathematically justified manner, while remaining computationally attractive. We evaluate our framework on a publicly available lung CT data set to demonstrate the properties of the new formulation. The presented results show the improved accuracy for our joint segmentation and registration algorithm when compared to registration and segmentation performed separately.
Highlights
Image registration and segmentation techniques are fundamental components of medical image analysis as they form the basis for many advanced frameworks for computerized understanding of medical imaging
We present a comparison of various methods that we have described in previous sections and assess them for joint segmentation and registration of lung Computed Tomography (CT) images
In this article we presented a novel joint segmentation and registration method using the level-set framework
Summary
Image registration and segmentation techniques are fundamental components of medical image analysis as they form the basis for many advanced frameworks for computerized understanding of medical imaging. In Vemuri et al (2003), segmentation-based registration using a level-set approach was proposed This was extended in Gorthi et al (2011) to a generalized registration framework: an active deformation field, which merges well different approaches for non-linear contour matching. We present the state-of-the-art level-set registration algorithm proposed in Vemuri et al (2003) (Section 2.3) together with its extension to a generalized framework for non-linear level-set registration developed in Gorthi et al (2011) (Section 2.4).
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