Non-rigid Free-Form 2D-3D Registration Using Statistical Deformation Model

This paper presents a non-rigid free-from 2D-3D registration approach using statistical deformation model (SDM). In our approach the SDM is first constructed from a set of training data using a non-rigid registration algorithm based on b-spline free-form deformation to encode a priori information about the underlying anatomy. A novel intensity-based non-rigid 2D-3D registration algorithm is then presented to iteratively fit the 3D b-spline-based SDM to the 2D X-ray images of an unseen subject, which requires a computationally expensive inversion of the instantiated deformation in each iteration. In this paper, we propose to solve this challenge with a fast B-spline pseudo-inversion algorithm that is implemented on graphics processing unit (GPU). Experiments conducted on C-arm and X-ray images of cadaveric femurs demonstrate the efficacy of the present approach.

Multimodal non-rigid image registration methods for therapy and surgery planning

La planificación pre-operatoria se ha convertido en una tarea esencial en cirugías y terapias de marcada complejidad, especialmente aquellas relacionadas con órgano blando. Un ejemplo donde la planificación preoperatoria tiene gran interés es la cirugía hepática. Dicha planificación comprende la detección e identificación precisa de las lesiones individuales y vasos así como la correcta segmentación y estimación volumétrica del hígado funcional. Este proceso es muy importante porque determina tanto si el paciente es un candidato adecuado para terapia quirúrgica como la definición del abordaje a seguir en el procedimiento. La radioterapia de órgano blando es un segundo ejemplo donde la planificación se requiere tanto para la radioterapia externa convencional como para la radioterapia intraoperatoria. La planificación comprende la segmentación de tumor y órganos vulnerables y la estimación de la dosimetría. La segmentación de hígado funcional y la estimación volumétrica para planificación de la cirugía se estiman habitualmente a partir de imágenes de tomografía computarizada (TC). De igual modo, en la planificación de radioterapia, los objetivos de la radiación se delinean normalmente sobre TC. Sin embargo, los avances en las tecnologías de imagen de resonancia magnética (RM) están ofreciendo progresivamente ventajas adicionales. Por ejemplo, se ha visto que el ratio de detección de metástasis hepáticas es significativamente superior en RM con contraste Gd–EOB–DTPA que en TC. Por tanto, recientes estudios han destacado la importancia de combinar la información de TC y RM para conseguir el mayor nivel posible de precisión en radioterapia y para facilitar una descripción precisa de las lesiones del hígado. Con el objetivo de mejorar la planificación preoperatoria en ambos escenarios se precisa claramente de un algoritmo de registro no rígido de imagen. Sin embargo, la gran mayoría de sistemas comerciales solo proporcionan métodos de registro rígido. Las medidas de intensidad de voxel han demostrado ser criterios de similitud de imágenes robustos, y, entre ellas, la Información Mutua (IM) es siempre la primera elegida en registros multimodales. Sin embargo, uno de los principales problemas de la IM es la ausencia de información espacial y la asunción de que las relaciones estadísticas entre las imágenes son homogéneas a lo largo de su domino completo. La hipótesis de esta tesis es que la incorporación de información espacial de órganos al proceso de registro puede mejorar la robustez y calidad del mismo, beneficiándose de la disponibilidad de las segmentaciones clínicas. En este trabajo, se propone y valida un esquema de registro multimodal no rígido 3D usando una nueva métrica llamada Información Mutua Centrada en el Órgano (Organ-Focused Mutual Information metric (OF-MI)) y se compara con la formulación clásica de la Información Mutua. Esto permite mejorar los resultados del registro en áreas problemáticas incorporando información regional al criterio de similitud, beneficiándose de la disponibilidad real de segmentaciones en protocolos estándares clínicos, y permitiendo que la dependencia estadística entre las dos modalidades de imagen difiera entre órganos o regiones. El método propuesto se ha aplicado al registro de TC y RM con contraste Gd–EOB–DTPA así como al registro de imágenes de TC y MR para planificación de radioterapia intraoperatoria rectal. Adicionalmente, se ha desarrollado un algoritmo de apoyo de segmentación 3D basado en Level-Sets para la incorporación de la información de órgano en el registro. El algoritmo de segmentación se ha diseñado específicamente para la estimación volumétrica de hígado sano funcional y ha demostrado un buen funcionamiento en un conjunto de imágenes de TC abdominales. Los resultados muestran una mejora estadísticamente significativa de OF-MI comparada con la Información Mutua clásica en las medidas de calidad de los registros; tanto con datos simulados (p<0.001) como con datos reales en registro hepático de TC y RM con contraste Gd– EOB–DTPA y en registro para planificación de radioterapia rectal usando OF-MI multi-órgano (p<0.05). Adicionalmente, OF-MI presenta resultados más estables con menor dispersión que la Información Mutua y un comportamiento más robusto con respecto a cambios en la relación señal-ruido y a la variación de parámetros. La métrica OF-MI propuesta en esta tesis presenta siempre igual o mayor precisión que la clásica Información Mutua y consecuentemente puede ser una muy buena alternativa en aplicaciones donde la robustez del método y la facilidad en la elección de parámetros sean particularmente importantes. Abstract Pre-operative planning has become an essential task in complex surgeries and therapies, especially for those affecting soft tissue. One example where soft tissue preoperative planning is of high interest is liver surgery. It involves the accurate detection and identification of individual liver lesions and vessels as well as the proper functional liver segmentation and volume estimation. This process is very important because it determines whether the patient is a suitable candidate for surgical therapy and the type of procedure. Soft tissue radiation therapy is a second example where planning is required for both conventional external and intraoperative radiotherapy. It involves the segmentation of the tumor target and vulnerable organs and the estimation of the planned dose. Functional liver segmentations and volume estimations for surgery planning are commonly estimated from computed tomography (CT) images. Similarly, in radiation therapy planning, targets to be irradiated and healthy and vulnerable tissues to be protected from irradiation are commonly delineated on CT scans. However, developments in magnetic resonance imaging (MRI) technology are progressively offering advantages. For instance, the hepatic metastasis detection rate has been found to be significantly higher in Gd–EOB–DTPAenhanced MRI than in CT. Therefore, recent studies highlight the importance of combining the information from CT and MRI to achieve the highest level of accuracy in radiotherapy and to facilitate accurate liver lesion description. In order to improve those two soft tissue pre operative planning scenarios, an accurate nonrigid image registration algorithm is clearly required. However, the vast majority of commercial systems only provide rigid registration. Voxel intensity measures have been shown to be robust measures of image similarity, and among them, Mutual Information (MI) is always the first candidate in multimodal registrations. However, one of the main drawbacks of Mutual Information is the absence of spatial information and the assumption that statistical relationships between images are the same over the whole domain of the image. The hypothesis of the present thesis is that incorporating spatial organ information into the registration process may improve the registration robustness and quality, taking advantage of the clinical segmentations availability. In this work, a multimodal nonrigid 3D registration framework using a new Organ- Focused Mutual Information metric (OF-MI) is proposed, validated and compared to the classical formulation of the Mutual Information (MI). It allows improving registration results in problematic areas by adding regional information into the similitude criterion taking advantage of actual segmentations availability in standard clinical protocols and allowing the statistical dependence between the two modalities differ among organs or regions. The proposed method is applied to CT and T1 weighted delayed Gd–EOB–DTPA-enhanced MRI registration as well as to register CT and MRI images in rectal intraoperative radiotherapy planning. Additionally, a 3D support segmentation algorithm based on Level-Sets has been developed for the incorporation of the organ information into the registration. The segmentation algorithm has been specifically designed for the healthy and functional liver volume estimation demonstrating good performance in a set of abdominal CT studies. Results show a statistical significant improvement of registration quality measures with OF-MI compared to MI with both simulated data (p<0.001) and real data in liver applications registering CT and Gd–EOB–DTPA-enhanced MRI and in registration for rectal radiotherapy planning using multi-organ OF-MI (p<0.05). Additionally, OF-MI presents more stable results with smaller dispersion than MI and a more robust behavior with respect to SNR changes and parameters variation. The proposed OF-MI always presents equal or better accuracy than the classical MI and consequently can be a very convenient alternative within applications where the robustness of the method and the facility to choose the parameters are particularly important.

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

Evaluation of sampling method effects in 3D non-rigid registration

Since the beginning of 3D computer vision problems, the use of techniques to reduce the data to make it treatable preserving the important aspects of the scene has been necessary. Currently, with the new low-cost RGB-D sensors, which provide a stream of color and 3D data of approximately 30 frames per second, this is getting more relevance. Many applications make use of these sensors and need a preprocessing to downsample the data in order to either reduce the processing time or improve the data (e.g., reducing noise or enhancing the important features). In this paper, we present a comparison of different downsampling techniques which are based on different principles. Concretely, five different downsampling methods are included: a bilinear-based method, a normal-based, a color-based, a combination of the normal and color-based samplings, and a growing neural gas (GNG)-based approach. For the comparison, two different models have been used acquired with the Blensor software. Moreover, to evaluate the effect of the downsampling in a real application, a 3D non-rigid registration is performed with the data sampled. From the experimentation we can conclude that depending on the purpose of the application some kernels of the sampling methods can improve drastically the results. Bilinear- and GNG-based methods provide homogeneous point clouds, but color-based and normal-based provide datasets with higher density of points in areas with specific features. In the non-rigid application, if a color-based sampled point cloud is used, it is possible to properly register two datasets for cases where intensity data are relevant in the model and outperform the results if only a homogeneous sampling is used.

Non-rigid object tracking in complex scenes

A recently proposed colour based tracking algorithm has been established to track objects in real circumstances [Zivkovic, Z., Krose, B. 2004. An EM-like algorithm for color-histogram-based object tracking. In: Proc, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 798-803]. To improve the performance of this technique in complex scenes, in this paper we propose a new algorithm for optimally adapting the ellipse outlining the objects of interest. This paper presents a Lagrangian based method to integrate a regularising component into the covariance matrix to be computed. Technically, we intend to reduce the residuals between the estimated probability distribution and the expected one. We argue that, by doing this, the shape of the ellipse can be properly adapted in the tracking stage. Experimental results show that the proposed method has favourable performance in shape adaption and object localisation.

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Rigid and Non-rigid Point-based Medical Image Registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

A coupling technique for non-matching finite element meshes

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Image registration for triggered and non-triggered DTI of the human kidney: Reduced variability of diffusion parameter estimation

BACKGROUND: To investigate if non-rigid image-registration reduces motion artifacts in triggered and non-triggered diffusion tensor imaging (DTI) of native kidneys. A secondary aim was to determine, if improvements through registration allow for omitting respiratory-triggering. METHODS: Twenty volunteers underwent coronal DTI of the kidneys with nine b-values (10-700 s/mm2 ) at 3 Tesla. Image-registration was performed using a multimodal nonrigid registration algorithm. Data processing yielded the apparent diffusion coefficient (ADC), the contribution of perfusion (FP ), and the fractional anisotropy (FA). For comparison of the data stability, the root mean square error (RMSE) of the fitting and the standard deviations within the regions of interest (SDROI ) were evaluated. RESULTS: RMSEs decreased significantly after registration for triggered and also for non-triggered scans (P < 0.05). SDROI for ADC, FA, and FP were significantly lower after registration in both medulla and cortex of triggered scans (P < 0.01). Similarly the SDROI of FA and FP decreased significantly in non-triggered scans after registration (P < 0.05). RMSEs were significantly lower in triggered than in non-triggered scans, both with and without registration (P < 0.05). CONCLUSION: Respiratory motion correction by registration of individual echo-planar images leads to clearly reduced signal variations in renal DTI for both triggered and particularly non-triggered scans. Secondarily, the results suggest that respiratory-triggering still seems advantageous.J. Magn. Reson. Imaging 2014. (c) 2014 Wiley Periodicals, Inc.

Use of resin-bonded extracoronal attachments with removable partial denture: case report

Introduction: Resin-bonded extracoronal attachments may be indicated for the abutment teeth of removable partial dentures, especially for anterior teeth when a cingulum rest must be provided. This type of treatment has a series of advantages such as minimal tooth reduction, supragingival margins, favourable stress distribution, and improved aesthetic appearance. Objective: To report a clinical case of oral rehabilitation using a combination of resin-bonded extracoronal attachments joined by a Dolder bar with a removable partial denture. Case report: A 60-year-old male patient with only the canines in the maxillary arch was restored with a combination of resin-bonded extracoronal attachments joined by a Dolder bar and a removable partial denture. Conclusion: Dentures with resin-bonded extracoronal attachments can have a number of advantages over traditional clasp-retained removable partial dentures.

Towards Detecting Changes in Underwater Image Sequences

Detecting changes between images of the same scene taken at different times is of great interest for monitoring and understanding the environment. It is widely used for on-land application but suffers from different constraints. Unfortunately, Change detection algorithms require highly accurate geometric and photometric registration. This requirement has precluded their use in underwater imagery in the past. In this paper, the change detection techniques available nowadays for on-land application were analyzed and a method to automatically detect the changes in sequences of underwater images is proposed. Target application scenarios are habitat restoration sites, or area monitoring after sudden impacts from hurricanes or ship groundings. The method is based on the creation of a 3D terrain model from one image sequence over an area of interest. This model allows for synthesizing textured views that correspond to the same viewpoints of a second image sequence. The generated views are photometrically matched and corrected against the corresponding frames from the second sequence. Standard change detection techniques are then applied to find areas of difference. Additionally, the paper shows that it is possible to detect false positives, resulting from non-rigid objects, by applying the same change detection method to the first sequence exclusively. The developed method was able to correctly find the changes between two challenging sequences of images from a coral reef taken one year apart and acquired with two different cameras

Recovering Euclidean deformable models from stereo-motion

In this paper we present a novel structure from motion (SfM) approach able to infer 3D deformable models from uncalibrated stereo images. Using a stereo setup dramatically improves the 3D model estimation when the observed 3D shape is mostly deforming without undergoing strong rigid motion. Our approach first calibrates the stereo system automatically and then computes a single metric rigid structure for each frame. Afterwards, these 3D shapes are aligned to a reference view using a RANSAC method in order to compute the mean shape of the object and to select the subset of points on the object which have remained rigid throughout the sequence without deforming. The selected rigid points are then used to compute frame-wise shape registration and to extract the motion parameters robustly from frame to frame. Finally, all this information is used in a global optimization stage with bundle adjustment which allows to refine the frame-wise initial solution and also to recover the non-rigid 3D model. We show results on synthetic and real data that prove the performance of the proposed method even when there is no rigid motion in the original sequence

Using 3D surface datasets to understand landslide evolution: From analogue models to real case study

Early detection of landslide surface deformation with 3D remote sensing techniques, as TLS, has become a great challenge during last decade. To improve our understanding of landslide deformation, a series of analogue simulation have been carried out on non-rigid bodies coupled with 3D digitizer. All these experiments have been carried out under controlled conditions, as water level and slope angle inclination. We were able to follow 3D surface deformation suffered by complex landslide bodies from precursory deformation still larger failures. These experiments were the basis for the development of a new algorithm for the quantification of surface deformation using automatic tracking method on discrete points of the slope surface. To validate the algorithm, comparisons were made between manually obtained results and algorithm surface displacement results. Outputs will help in understanding 3D deformation during pre-failure stages and failure mechanisms, which are fundamental aspects for future implementation of 3D remote sensing techniques in early warning systems.