Discontinuity preserving image registration for breathing induced sliding organ motion

Image registration is a powerful tool in medical image analysis and facilitates the clinical routine in several aspects. It became an indispensable device for many medical applications including image-guided therapy systems. The basic goal of image registration is to spatially align two images that show a similar region of interest. More speci�cally, a displacement �eld respectively a transformation is estimated, that relates the positions of the pixels or feature points in one image to the corresponding positions in the other one. The so gained alignment of the images assists the doctor in comparing and diagnosing them. There exist di�erent kinds of image registration methods, those which are capable to estimate a rigid transformation or more generally an a�ne transformation between the images and those which are able to capture a more complex motion by estimating a non-rigid transformation. There are many well established non-rigid registration methods, but those which are able to preserve discontinuities in the displacement �eld are rather rare. These discontinuities appear in particular at organ boundaries during the breathing induced organ motion. In this thesis, we make use of the idea to combine motion segmentation with registration to tackle the problem of preserving the discontinuities in the resulting displacement �eld. We introduce a binary function to represent the motion segmentation and the proposed discontinuity preserving non-rigid registration method is then formulated in a variational framework. Thus, an energy functional is de�ned and its minimisation with respect to the displacement �eld and the motion segmentation will lead to the desired result. In theory, one can prove that for the motion segmentation a global minimiser of the energy functional can be found, if the displacement �eld is given. The overall minimisation problem, however, is non-convex and a suitable optimisation strategy has to be considered. Furthermore, depending on whether we use the pure L1-norm or an approximation of it in the formulation of the energy functional, we use di�erent numerical methods to solve the minimisation problem. More speci�cally, when using an approximation of the L1-norm, the minimisation of the energy functional with respect to the displacement �eld is performed through Brox et al.'s �xed point iteration scheme, and the minimisation with respect to the motion segmentation with the dual algorithm of Chambolle. On the other hand, when we make use of the pure L1-norm in the energy functional, the primal-dual algorithm of Chambolle and Pock is used for both, the minimisation with respect to the displacement �eld and the motion segmentation. This approach is clearly faster compared to the one using the approximation of the L1-norm and also theoretically more appealing. Finally, to support the registration method during the minimisation process, we incorporate additionally in a later approach the information of certain landmark positions into the formulation of the energy functional, that makes use of the pure L1-norm. Similarly as before, the primal-dual algorithm of Chambolle and Pock is then used for both, the minimisation with respect to the displacement �eld and the motion segmentation. All the proposed non-rigid discontinuity preserving registration methods delivered promising results for experiments with synthetic images and real MR images of breathing induced liver motion

Detection of dirt impairments from archived film sequences : survey and evaluations

Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research

Motion Cooperation: Smooth Piece-Wise Rigid Scene Flow from RGB-D Images

We propose a novel joint registration and segmentation approach to estimate scene flow from RGB-D images. Instead of assuming the scene to be composed of a number of independent rigidly-moving parts, we use non-binary labels to capture non-rigid deformations at transitions between the rigid parts of the scene. Thus, the velocity of any point can be computed as a linear combination (interpolation) of the estimated rigid motions, which provides better results than traditional sharp piecewise segmentations. Within a variational framework, the smooth segments of the scene and their corresponding rigid velocities are alternately refined until convergence. A K-means-based segmentation is employed as an initialization, and the number of regions is subsequently adapted during the optimization process to capture any arbitrary number of independently moving objects. We evaluate our approach with both synthetic and real RGB-D images that contain varied and large motions. The experiments show that our method estimates the scene flow more accurately than the most recent works in the field, and at the same time provides a meaningful segmentation of the scene based on 3D motion.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Spanish Government under the grant programs FPI-MICINN 2012 and DPI2014- 55826-R (co-founded by the European Regional Development Fund), as well as by the EU ERC grant Convex Vision (grant agreement no. 240168)

Lucas/Kanade meets Horn/Schunck : combining local and global optic flow methods

Differential methods belong to the most widely used techniques for optic flow computation in image sequences. They can be classified into local methods such as the Lucas-Kanade technique or Bigün\u27s structure tensor method, and into global methods such as the Horn/Schunck approach and its extensions. Often local methods are more robust under noise, while global techniques yield dense flow fields. The goal of this paper is to contribute to a better understanding and the design of differential methods in four ways: (i) We juxtapose the role of smoothing/regularisation processes that are required in local and global differential methods for optic flow computation. (ii) This discussion motivates us to describe and evaluate a novel method that combines important advantages of local and global approaches: It yields dense flow fields that are robust against noise. (iii) Spatiotemproal and nonlinear extensions to this hybrid method are presented. (iv) We propose a simple confidence measure for optic flow methods that minimise energy functionals. It allows to sparsify a dense flow field gradually, depending on the reliability required for the resulting flow. Comparisons with experiments from the literature demonstrate the favourable performance of the proposed methods and the confidence measure

Variational Disparity Estimation Framework for Plenoptic Image

This paper presents a computational framework for accurately estimating the disparity map of plenoptic images. The proposed framework is based on the variational principle and provides intrinsic sub-pixel precision. The light-field motion tensor introduced in the framework allows us to combine advanced robust data terms as well as provides explicit treatments for different color channels. A warping strategy is embedded in our framework for tackling the large displacement problem. We also show that by applying a simple regularization term and a guided median filtering, the accuracy of displacement field at occluded area could be greatly enhanced. We demonstrate the excellent performance of the proposed framework by intensive comparisons with the Lytro software and contemporary approaches on both synthetic and real-world datasets