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a b s t r a c tThis paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.

A B S T R A C TThis study proposes an extension of the Weighted Ensemble Kalman filter (WEnKF) proposed by Papadakis et al. (2010) for the assimilation of image observations. The main focus of this study is on a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF), incorporating directly as a measurement model a non-linear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2-D turbulence observed through the transport of a passive scalar. In particular, it has been applied for the reconstruction of oceanic surface current vorticity fields from sea surface temperature (SST) satellite data. This latter technique enables a consistent recovery along time of oceanic surface currents and vorticity maps in presence of large missing data areas and strong noise.

In fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality. DESCRIPTION OF THE PURPOSEMost of the high-resolution reconstruction algorithms used in fetal MRI 1-10 rely only on brain tissue-relevant voxels of low-resolution (LR) images. In general, those algorithms need to perform brain segmentation as a preprocessing step. This brain extraction is essential to ensure good results of the subsequent image processing steps (denoising, bias correction, motion estimation, and super-resolution reconstruction). Despite of manual brain segmentation can be performed, it is highly time-consuming (around 15 minutes per stack of 15 slices) and not a realistic solution for large-scale studies. In the literature, accurate brain extraction tools have been developed for adult and infant brain MRI. 11, 12 But, fetal brain MRI differs a lot from neonatal or adult imaging in terms of image content (with maternal tissues surrounding the fetal brain), image contrast and brain size. Consequently, those tools are not well adapted to fetal MRI.Few works have addressed the automatic extraction of fetal brain in MRI. Two major types of approaches can be distinguished, either template-based segmentation 13-15 or machine learning 16-18 techniques. The first attempt 13 of fetal brain extraction proposed first to estimate the location of the eyes (based on rigid template registration) in order to segment the fetal brain using contrast, morphological and biometrical prior information. This method gave precise results in 22 out of 24 stacks of fetuses aged between 30 and 53 gestational weeks (GW). However, they relied on the assumption of low motion between slices that limits the robustness of the method to clinical databases where large motion can occur. More recently, Taleb et al. 14 presented an efficient brain extraction method based on single age-specific template segmentation (affine template registration) and fusion of orthogonal segmentations (transversal, coronal and sagittal) where final brain masks were successfully estimated in 82% of the cases. The validation was however rather qualitative (i.e. only success or failure label was given to the results). A supervised approach, 16 based on a two-phase random forest classifier, was adopted in order to obtain a method applicable to all fetal ages and more robust with respect to motion between slices. This method has shown comparable results to the method 13 but the whole brain was c...

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Although visual object tracking algorithms are capable of handling various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking by detection method, the key issue lies in detecting the target over the whole frame and updating systematically a target model based on the last detected appearance to avoid the drift phenomenon. This paper aims at proposing a novel robust tracking algorithm by fusing the frame level detection strategy of tracking, learning, & detection with the systematic model update strategy of Kernelized Correlation Filter tracker. The risk of drift is mitigated by the fact that the model updates are primarily driven by the detections that occur in the spatial neighborhood of the latest detections. The motivation behind the selection of trackers is their complementary nature in handling tracking challenges. The proposed algorithm efficiently combines the two state-of-the-art tracking algorithms based on conservative correspondence measure with strategic model updates, which takes advantages of both and outperforms them on their short ends by virtue of other. Extensive evaluation of the proposed method based on different metrics is carried out on the data sets ALOV300++, Visual Tracker Benchmark, and Visual Object Tracking. We demonstrated its performance in terms of robustness and success rate by comparing with state-of-the-art trackers.

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