Motion Driven Tonal Stabilization.

This paper addresses the problem of tonal fluctuation in videos. Due to the automatic settings of consumer cameras, the colors of objects in image sequences might change over time. We propose here a fast and computationally light method to stabilize this tonal appearance, while remaining robust to motion and occlusions. To do so, a minimally viable color correction model is used, in conjunction with an effective estimation of dominant motion. The final solution is a temporally weighted correction, explicitly driven by the motion magnitude, both visually efficient and very fast, with potential to real time processing. Experimental results obtained on a variety of sequences outperform the current state of the art in terms of tonal stability, at a much reduced computational complexity.

3D Rigid Registration of Intraoperative Ultrasound and Preoperative MR Brain Images Based on Hyperechogenic Structures.

The registration of intraoperative ultrasound (US) images with preoperative magnetic resonance (MR) images is a challenging problem due to the difference of information contained in each image modality. To overcome this difficulty, we introduce a new probabilistic function based on the matching of cerebral hyperechogenic structures. In brain imaging, these structures are the liquid interfaces such as the cerebral falx and the sulci, and the lesions when the corresponding tissue is hyperechogenic. The registration procedure is achieved by maximizing the joint probability for a voxel to be included in hyperechogenic structures in both modalities. Experiments were carried out on real datasets acquired during neurosurgical procedures. The proposed validation framework is based on (i) visual assessment, (ii) manual expert estimations , and (iii) a robustness study. Results show that the proposed method (i) is visually efficient, (ii) produces no statistically different registration accuracy compared to manual-based expert registration, and (iii) converges robustly. Finally, the computation time required by our method is compatible with intraoperative use.

An anthropomorphic polyvinyl alcohol brain phantom based on Colin27 for use in multimodal imaging.

PURPOSE: In this paper, the method for the creation of an anatomically and mechanically realistic brain phantom from polyvinyl alcohol cryogel (PVA-C) is proposed for validation of image processing methods such as segmentation, reconstruction, registration, and denoising. PVA-C is material widely used in medical imaging phantoms because of its mechanical similarities to soft tissues. METHODS: The phantom was cast in a mold designed using the left hemisphere of the Colin27 brain dataset [C. Holmes et al., "Enhancement of MR images using registration for signal averaging," J. Comput. Assist. Tomogr. 22(2), 324 (1998)]. Marker spheres and inflatable catheters were also implanted to enable good registration comparisons and to simulate tissue deformation, respectively. RESULTS: The phantom contained deep sulci, a complete insular region, and an anatomically accurate left ventricle. It was found to provide good contrast in triple modality imaging, consisting of computed tomography, ultrasound, and magnetic resonance imaging. Multiple sets of multimodal data were acquired from this phantom. CONCLUSIONS: The methods for building the anatomically accurate, multimodality phantom were described in this work. All multimodal data are made available freely to the image processing community (http://pvabrain.inria.fr). We believe the phantom images could allow for the validation and further aid in the development of novel medical image processing techniques.

Assessment of standard coil positioning in transcranial magnetic stimulation in depression.

Transcranial magnetic stimulation (TMS) is a non-invasive technique used in the treatment of major depression. Meta-analyses have shown that it is more efficient than a placebo and that its efficacy is enhanced by the optimum tuning of stimulation parameters. However, the stimulation target, the dorsolateral prefrontal cortex (DLPFC), is still located using an inaccurate method. In this study, a neuronavigation system was used to perform a comprehensive quantification of target localization errors. We identified and quantified 3 sources of error in the standard method: cap repositioning, interexpert variability in coil positioning and distance between the stimulated point and the expected target. For cap repositioning, the standard deviation was lower than 5mm in the 3 axes. For interexpert variability in coil positioning, the spatial dispersion of the points was higher than 10mm in 2 of the 3 axes. For interindividual anatomical variability, the distance between the actual "reference" DLPFC and its standard determination was greater than 20mm for 54% of the subjects, while one subject out of eleven was correctly targeted which means 10mm or less from the reference. Results showed that interindividual anatomical variability and interexpert variability were the two main sources of error using the standard method. Results demonstrate that a neuronavigation system is mandatory to conduct reproducible and reliable studies.

An anthropomorphic polyvinyl alcohol triple-modality brain phantom based on Colin27.

We propose a method for the creation of an anatomically and mechanically realistic brain phantom from polyvinyl alcohol cryogel (PVA-C) for validation of image processing methods for segmentation, reconstruction, registration, and denoising. PVA-C is material widely used in medical imaging phantoms for its mechanical similarities to soft tissues. The phantom was cast in a mold designed using the left hemiphere of the Colin27 brain dataset and contains deep sulci, a complete insular region, and an anatomically accurate left ventricle. Marker spheres and inflatable catheters were also implanted to enable good registration and simulate tissue deformation, respectively. The phantom was designed for triple modality imaging, giving good contrast images in computed tomography, ultrasound, and magnetic resonance imaging. Multimodal data acquired from this phantom are made freely available to the image processing community (http://pvabrain. inria.fr) and will aid in the validation and further development of medical image processing techniques.

An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images.

In ultrasound images, acoustic shadows appear as regions of low signal intensity linked to boundaries with very high acoustic impedance differences. Acoustic shadows can be viewed either as informative features to detect lesions or calcifications, or as damageable artifacts for image processing tasks such as segmentation, registration or 3D reconstruction. In both cases, the detection of these acoustic shadows is useful. This paper proposes a new method to detect these shadows that combines a geometrical approach to estimate the B-scans shape, followed by a statistical test based on a dedicated modeling of ultrasound image statistics. Results demonstrate that the combined geometrical-statistical technique is more robust and yields better results than the previous statistical technique. Integration of regularization over time further improves robustness. Application of the procedure results in (1) improved 3D reconstructions with fewer artifacts, and (2) reduced mean registration error of tracked intraoperative brain ultrasound images.

Nonlocal means-based speckle filtering for ultrasound images.

In image processing, restoration is expected to improve the qualitative inspection of the image and the performance of quantitative image analysis techniques. In this paper, an adaptation of the nonlocal (NL)-means filter is proposed for speckle reduction in ultrasound (US) images. Originally developed for additive white Gaussian noise, we propose to use a Bayesian framework to derive a NL-means filter adapted to a relevant ultrasound noise model. Quantitative results on synthetic data show the performances of the proposed method compared to well-established and state-of-the-art methods. Results on real images demonstrate that the proposed method is able to preserve accurately edges and structural details of the image.

Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications.

Digital atlases are commonly used in pre-operative planning in functional neurosurgical procedures performed to minimize the symptoms of Parkinson's disease. These atlases can be customized to fit an individual patient's anatomy through atlas-to-patient warping procedures. Once fitted to pre-operative magnetic resonance imaging (MRI) data, the customized atlas can be used to plan and navigate surgical procedures. Linear, piece-wise linear and nonlinear registration methods have been used to customize different digital atlases with varying accuracies. Our goal was to evaluate eight different registration methods for atlas-to-patient customization of a new digital atlas of the basal ganglia and thalamus to demonstrate the value of nonlinear registration for automated atlas-based subcortical target identification in functional neurosurgery. In this work, we evaluate the accuracy of two automated linear techniques, two piece-wise linear techniques (requiring the identification of manually placed anatomical landmarks), and four different automated nonlinear atlas-to-patient warping techniques (where two of the four nonlinear techniques are variants of the ANIMAL algorithm). Since a gold standard of the subcortical anatomy is not available, manual segmentations of the striatum, globus pallidus, and thalamus are used to derive a silver standard for evaluation. Four different metrics, including the kappa statistic, the mean distance between the surfaces, the maximum distance between surfaces, and the total structure volume are used to compare the warping techniques. The results show that nonlinear techniques perform statistically better than linear and piece-wise linear techniques. In addition, the results demonstrate statistically significant differences between the nonlinear techniques, with the ANIMAL algorithm yielding better results.

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.

3D wavelet subbands mixing for image denoising.

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. The method proposed in this paper is a fully automatic 3D blockwise version of the nonlocal (NL) means filter with wavelet subbands mixing. The proposed wavelet subbands mixing is based on a multiresolution approach for improving the quality of image denoising filter. Quantitative validation was carried out on synthetic datasets generated with the BrainWeb simulator. The results show that our NL-means filter with wavelet subbands mixing outperforms the classical implementation of the NL-means filter in terms of denoising quality and computation time. Comparison with wellestablished methods, such as nonlinear diffusion filter and total variation minimization, shows that the proposed NL-means filter produces better denoising results. Finally, qualitative results on real data are presented.

Probe trajectory interpolation for 3D reconstruction of freehand ultrasound.

Three-dimensional (3D) freehand ultrasound uses the acquisition of non-parallel B-scans localized in 3D by a tracking system (optic, mechanical or magnetic). Using the positions of the irregularly spaced B-scans, a regular 3D lattice volume can be reconstructed, to which conventional 3D computer vision algorithms (registration and segmentation) can be applied. This paper presents a new 3D reconstruction method which explicitly accounts for the probe trajectory. Experiments were conducted on phantom and intra-operative datasets using various probe motion types and varied slice-to-slice B-scan distances. Results suggest that this technique improves on classical methods at the expense of computational time.

Quantitative evaluation of three calibration methods for 3-D freehand ultrasound.

In this paper, three different calibration methods for three-dimensional (3-D) freehand ultrasound (US) are evaluated. Calibration is the process of estimating the rigid transformation from US image coordinates to the coordinate system of the tracking sensor mounted onto the probe. Calibration accuracy has an important impact on quantitative studies. Geometrical precision can also be crucial in many interventions and surgery. The proposed evaluation framework relies on a single point phantom and a 3-D US phantom which mimics the US characteristics of human liver. Four quality measures are used: 3-D point localization criterion, distance and volume measurements, and shape based criterion. Results show that during the acquisition procedure, volumetric measurements and shapes of the reconstructed object depend on probe motion used, particularly fan motions for which errors are larger. It is also shown that accurate calibration is essential to obtain reliable quantitative information.

A novel temporal calibration method for 3-D ultrasound.

This paper examines a novel approach for temporal calibration of a three-dimensional (3-D) freehand ultrasound system. A localization system fixed on the probe gives the position and orientation of the probe. For quantitative use, calibration is needed to correctly localize a B-scan in four-dimensional (4-D) (3-D+t) space. Temporal latency estimation is defined in a general robust formulation using no specific probe motion constraints. Experiments were performed on synthetic and real data using a 3-D freehand ultrasound system. The achieved precision is lower than the image acquisition rate (40 ms). A validation study using a calibration phantom has been performed to evaluate the influence of incorrect latency estimation on the 3-D reconstruction procedure. We showed that for latency estimation errors less than 40 ms, the 3-D reconstruction errors are negligible for volume estimation.

A novel parametric method for non-rigid image registration.

This paper presents a novel non-rigid registration method. The main contribution of the method is the modeling of the vorticity (respectively divergence) of the deformation field using vortex (respectively sink and source) particles. Two parameters are associated with a particle: the vorticity (or divergence) strength and the influence domain. This leads to a very compact representation of vorticity and divergence fields. In addition, the optimal position of these particles is determined using a mean shift process. 2D experiments of this method are presented and demonstrate its ability to recover evolving phenomena (MS lesions) so as to register images from 20 patients.

3D freehand ultrasound reconstruction based on probe trajectory.

3D freehand ultrasound imaging is a very attractive technique in medical examinations and intra-operative stage for its cost and field of view capacities. This technique produces a set of non parallel B-scans which are irregularly distributed in the space. Reconstruction amounts to computing a regular lattice volume and is needed to apply conventional computer vision algorithms like registration. In this paper, a new 3D reconstruction method is presented, taking explicitly into account the probe trajectory. Experiments were conducted on different data sets with various probe motion types and indicate that this technique outperforms classical methods, especially on low acquisition frame rate.

Confhusius: a robust and fully automatic calibration method for 3D freehand ultrasound.

This paper describes a new robust and fully automatic method for calibration of three-dimensional (3D) freehand ultrasound called Confhusius (CalibratiON for FreeHand UltraSound Imaging USage). 3D Freehand ultrasound consists in mounting a position sensor on a standard probe. The echographic B-scans can be localized in 3D and compounded into a volume. However, especially for quantitative use, this process dramatically requires a calibration procedure that determines its accuracy and usefulness. Calibration aims at determining the transformation (translations, rotations, scaling) between the coordinates system of the echographic images and the coordinate system of the localization system. To calibrate, we acquire images of a phantom whose 3D geometrical properties are known. We propose a robust and fully automatic calibration method based on the Hough transform and robust estimators. Experiments have been done with synthetic and real sequences, and this calibration method is shown to be easy to perform, accurate, automatic and fast enough for clinical use.

A hierarchical parametric algorithm for deformable multimodal image registration.

Image fusion is of utmost importance for many applications in image analysis. Particularly in medical imaging, images of different modalities are necessary because they provide complementary information that must be merged for an optimal use. The fusion of these images, which can be achieved through a registration process, makes it possible to superimpose all available information on the same frame. In many cases, a rigid transformation is sufficient to align correctly the images. However, there are cases where a non-rigid transformation is needed: geometrical distortions present in one image, non-rigid motion, etc. The purpose of this paper is to propose a generic method to account for these deformations in case of multimodal images. We have applied the algorithm in the particular context of 3D medical images and present results on simulated and real data.

Coupling dense and landmark-based approaches for nonrigid registration.

In this paper, we investigate the introduction of cortical constraints for non rigid intersubject brain registration. We extract sulcal patterns with the active ribbon method, presented by Le Goualher et al. (1997). An energy based registration method (Hellier et al., 2001), which will be called photometric registration method in this paper, makes it possible to incorporate the matching of cortical sulci. The local sparse similarity and the photometric similarity are, thus, expressed in a unified framework. We show the benefits of cortical constraints on a database of 18 subjects, with global and local assessment of the registration. This new registration scheme has also been evaluated on functional magnetoencephalography data. We show that the anatomically constrained registration leads to a substantial reduction of the intersubject functional variability.