Non-rigid Registration Technique Research Articles

Nonrigid registration method for longitudinal chest CT images in COVID-19

Rationale and objectivesTo analyze morphological changes in patients with COVID-19-associated pneumonia over time, a nonrigid registration technique is required that reduces differences in respiratory phase and imaging position and does not excessively deform the lesion region. A nonrigid registration method using deep learning was applied for lung field alignment, and its practicality was verified through quantitative evaluation, such as image similarity of whole lung region and image similarity of lesion region, as well as visual evaluation by a physician. Materials and methodsFirst, the lung field positions and sizes of the first and second CT images were roughly matched using a classical registration method based on iterative calculations as a preprocessing step. Then, voxel-by-voxel transformation was performed using VoxelMorph, a nonrigid deep learning registration method. As an objective evaluation, the similarity of the images was calculated. To evaluate the invariance of image features in the lesion site, primary statistics and 3D shape features were calculated and statistically analyzed. Furthermore, as a subjective evaluation, the similarity of images and whether nonrigid transformation caused unnatural changes in the shape and size of the lesion region were visually evaluated by a pulmonologist. ResultsThe proposed method was applied to 509 patient data points with high image similarity. The variances in histogram characteristics before and after image deformation were confirmed. Visual evaluation confirmed the agreement between the shape and internal structure of the lung field and the natural deformation of the lesion region. ConclusionThe developed nonrigid registration method was shown to be effective for quantitative time series analysis of the lungs.

AI-based quantification of TILs using hematoxylin and eosin and immunohistochemistry-stained slides in triple-negative breast cancer.

e13608 Background: The importance of accurately understanding the status of tumor-infiltrating lymphocytes (TILs) in the preoperative chemotherapy context, for triple-negative breast cancer is underscored, highlighting the dual role of TILs in gauging treatment efficacy and prognostication. However, the difficulty in quantitatively and objectively assessing TILs in clinical practice is acknowledged, noting the barrier to its adoption as a standard diagnostic end-point. This study proposes a novel AI-based method to overcome these challenges, by quantitatively assessing TILs using hematoxylin and eosin (H&E) and immunohistochemistry (IHC) staining, and exploring their prognostic relationship. Methods: In this study, 68 cases of breast cancer with axillary lymph node metastasis at the time of resection, were selected from a total of 3,902 resections conducted between 2002 and 2016. To quantitatively assess TILs, we developed a quantitative assessment method, integrating an AI-based epithelial detection model and non-rigid image registration techniques to align H&E and IHC-stained images for precise measurement of immune markers (CD3, CD8, FOXP3, PD-L1) within the tumor stroma. Using an AI-based model trained on 12,273 annotated epithelial images, the stroma, excluding the epithelium, was identified. The tumor bed was also defined by a pathologist. Finally, we assessed the prognostic significance of the TILs for disease-free survival (DFS) through ROC analysis and log-rank tests. Results: Optimal thresholds for CD3 (0.152 mm²), CD8 (4.8 mm²), FOXP3 (0.25 mm²), and PD-L1 (0.115 mm²) were identified, with corresponding AUCs of 0.678, 0.625, 0.746, and 0.659. Patients with cell counts or areas above these thresholds demonstrated improved DFS (P<0.05 for all markers). Conclusions: These findings suggest that the quantification of immune cells within the tumor bed provides reliable prognostic indicators for recurrence. The AI-based TILs quantification offers a significant advancement in the prognostic assessment of triple-negative breast cancer. In addition, it improves the objectivity and clinical utility of pathological evaluations. This precise measurement of TILs could be transformative in patient prognostication and the formulation of treatment plans.

Comparative Analysis of Non-Rigid Registration Techniques for Liver Surface Registration.

Non-rigid surface-based soft tissue registration is crucial for surgical navigation systems, but its adoption still faces several challenges due to the large number of degrees of freedom and the continuously varying and complex surface structures present in the intra-operative data. By employing non-rigid registration, surgeons can integrate the pre-operative images into the intra-operative guidance environment, providing real-time visualization of the patient's complex pre- and intra-operative anatomy in a common coordinate system to improve navigation accuracy. However, many of the existing registration methods, including those for liver applications, are inaccessible to the broader community. To address this limitation, we present a comparative analysis of several open-source, non-rigid surface-based liver registration algorithms, with the overall goal of contrasting their strength and weaknesses and identifying an optimal solution. We compared the robustness of three optimization-based and one data-driven nonrigid registration algorithms in response to a reduced visibility ratio (reduced partial views of the surface) and to an increasing deformation level (mean displacement), reported as the root mean square error (RMSE) between the pre-and intra-operative liver surface meshed following registration. Our results indicate that the Gaussian Mixture Model - Finite Element Model (GMM-FEM) method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.

Prostate volume analysis in image registration for prostate cancer care: a verification study

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.

SPaM: soft patch matching for non-rigid pointcloud registration.

3d reconstruction of deformable objects in dynamic scenes forms the fundamental basis of many robotic applications. Existing mesh-based approaches compromise registration accuracy, and lose important details due to interpolation and smoothing. Additionally, existing non-rigid registration techniques struggle with unindexed points and disconnected manifolds. We propose a novel non-rigid registration framework for raw, unstructured, deformable point clouds purely based on geometric features. The global non-rigid deformation of an object is formulated as an aggregation of locally rigid transformations. The concept of locality is embodied in soft patches described by geometrical properties based on SHOT descriptor and its neighborhood. By considering the confidence score of pairwise association between soft patches of two scans (not necessarily consecutive), a computed similarity matrix serves as the seed to grow a correspondence graph which leverages rigidity terms defined in As-Rigid-As-Possible for pruning and optimization. Experiments on simulated and publicly available datasets demonstrate the capability of the proposed approach to cope with large deformations blended with numerous missing parts in the scan process.

Non-Rigid Registration for High-Resolution Retinal Imaging.

Adaptive optics provides improved resolution in ophthalmic imaging when retinal microstructures need to be identified, counted, and mapped. In general, multiple images are averaged to improve the signal-to-noise ratio or analyzed for temporal dynamics. Image registration by cross-correlation is straightforward for small patches; however, larger images require more sophisticated registration techniques. Strip-based registration has been used successfully for photoreceptor mosaic alignment in small patches; however, if the deformations along strips are not simple displacements, averaging can degrade the final image. We have applied a non-rigid registration technique that improves the quality of processed images for mapping cones over large image patches. In this approach, correction of local deformations compensates for local image stretching, compressing, bending, and twisting due to a number of causes. The main result of this procedure is improved definition of retinal microstructures that can be better identified and segmented. Derived metrics such as cone density, wall-to-lumen ratio, and quantification of structural modification of blood vessel walls have diagnostic value in many retinal diseases, including diabetic retinopathy and age-related macular degeneration, and their improved evaluations may facilitate early diagnostics of retinal diseases.

Comparison of Accuracies between Real-Time Nonrigid and Rigid Registration in the MRI-US Fusion Biopsy of the Prostate.

Magnetic resonance imaging (MRI) is increasingly important in the detection and localization of prostate cancer. Regarding suspicious lesions on MRI, a targeted biopsy using MRI fused with ultrasound (US) is widely used. To achieve a successful targeted biopsy, a precise registration between MRI and US is essential. The purpose of our study was to show any decrease in errors using a real-time nonrigid registration technique for prostate biopsy. Nineteen patients with suspected prostate cancer were prospectively enrolled in this study. Registration accuracy was calculated by the measuring distance of corresponding points by rigid and nonrigid registration between MRI and US, and compared for rigid and nonrigid registration methods. Overall cancer detection rates were also evaluated by patient and by core. Prostate volume was measured automatically from MRI and manually from US, and compared to each other. Mean distances between the corresponding points in MRI and US were 5.32 ± 2.61 mm for rigid registration and 2.11 ± 1.37 mm for nonrigid registration (p < 0.05). Cancer was diagnosed in 11 of 19 patients (57.9%), and in 67 of 266 biopsy cores (25.2%). There was no significant difference in prostate-volume measurement between the automatic and manual methods (p = 0.89). In conclusion, nonrigid registration reduces targeting errors.

Optimizing early cancer diagnosis and detection using a temporal subtraction technique

To optimize the early diagnosis and detection of lung cancer, computer-aided diagnostic (CAD) systems have been a useful tool for analyzing medical images. The temporal subtraction technique, which is a CAD system, performs the subtraction operation between the current image and the previous image on the same patient, and supports observation by emphasizing the temporal changes. However, the temporal subtraction technique for 3D images, such as thoracic CT images, has not yet been established. There is a need to develop efficient and highly accurate 3D nonrigid registration techniques to reduce subtraction artifacts. This study aims to develop a 3D nonrigid registration technique to establish a 3D temporal subtraction technique. In particular, we focus on the Finite Element Method, which is versatile, applicable to a wide range of fields, and capable of handling any shape. Our new method was examined on 46 clinical cases with multidetector row computed tomography images. As a result, the proposed method improved by 6.93% (p = 3.0 × 10−6) compared to the conventional methods in terms of the rate of reduction of artifacts, and the effectiveness was verified. Therefore, this study contributes to the literature on early detection and treatment.

CNN-Based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI.

Patient-specific left ventricle (LV) myocardial models have the potential to be used in a variety of clinical scenarios for improved diagnosis and treatment plans. Cine cardiac magnetic resonance (MR) imaging provides high resolution images to reconstruct patient-specific geometric models of the LV myocardium. With the advent of deep learning, accurate segmentation of cardiac chambers from cine cardiac MR images and unsupervised learning for image registration for cardiac motion estimation on a large number of image datasets is attainable. Here, we propose a deep leaning-based framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images, using the Automated Cardiac Diagnosis Challenge (ACDC) dataset. We use the deformation field estimated from the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole (ED) frame to the subsequent frames of the cardiac cycle. We assess the CNN-based propagated models against segmented models at each cardiac phase, as well as models propagated using another traditional nonrigid image registration technique. Additionally, we generate dynamic LV myocardial volume meshes at all phases of the cardiac cycle using the log barrier-based mesh warping (LBWARP) method and compare them with the CNN-propagated volume meshes.

Evaluation of fully automated myocardial segmentation techniques in native and contrast‐enhanced T1‐mapping cardiovascular magnetic resonance images using fully convolutional neural networks

T1-mapping cardiac magnetic resonance (CMR) imaging permits noninvasive quantification of myocardial fibrosis (MF); however, manual delineation of myocardial boundaries is time-consuming and introduces user-dependent variability for such measurements. In this study, we compare several automated pipelines for myocardial segmentation of the left ventricle (LV) in native and contrast-enhanced T1-maps using fully convolutional neural networks (CNNs). Sixty patients with known MF across three distinct cardiomyopathy states (20 ischemic (ICM), 20 dilated (DCM), and 20 hypertrophic (HCM)) underwent a standard CMR imaging protocol inclusive of cinematic (CINE), late gadolinium enhancement (LGE), and pre/post-contrast T1 imaging. Native and contrast-enhanced T1-mapping was performed using a shortened modified Look-Locker imaging (shMOLLI) technique at the basal, mid-level, and/or apex of the LV. Myocardial segmentations in native and post-contrast T1-maps were performed using three state-of-the-art CNN-based methods: standard U-Net, densely connected neural networks (Dense Nets), and attention networks (Attention Nets) after dividing the dataset using fivefold cross validation. These direct segmentation techniques were compared to an alternative registration-based segmentation method, wherein spatially corresponding CINE images are segmented automatically using U-Net, and a nonrigid registration technique transforms and propagates CINE contours to the myocardial regions of T1-maps. The methodologies were validated in 125 native and 100 contrast-enhanced T1-maps using standard segmentation accuracy metrics. Pearson correlation coefficient r and Bland-Altman analysis were used to compare the computed global T1 values derived by manual, U-Net, and CINE registration methodologies. The U-Net-based method yielded optimal results in myocardial segmentation of native, contrast-enhanced, and CINE images compared to Dense Nets and Attention Nets. The direct U-Net-based method outperformed the CINE registration-based method in native T1-maps, yielding Dice similarity coefficient (DSC) of 82.7±12% compared to 81.4±6.9% (P<0.0001). However, in contrast-enhanced T1-maps, the CINE-registration-based method outperformed direct U-Net segmentation, yielding DSC of 77.0±9.6% vs 74.2±18% across all patient groups (P=0.0014) and specifically 73.2±7.3% vs 65.5±18% in the ICM patient group. High linear correlation of global T1 values was demonstrated in Pearson analysis of the U-Net-based technique and the CINE-registration technique in both native T1-maps (r=0.93, P<0.0001 and r=0.87, P<0.0001, respectively) and contrast-enhanced T1-maps (r=0.93, P<0.0001 and r=0.98, P<0.0001, respectively). The direct U-Net-based myocardial segmentation technique provided accurate, fully automated segmentations in native and contrast-enhanced T1-maps. Myocardial borders can alternatively be segmented from spatially matched CINE images and applied to T1-maps via deformation and propagation through a modality-independent neighborhood descriptor (MIND). The direct U-Net approach is more efficient in myocardial segmentation of native T1-maps and eliminates cross-technique dependence. However, the CINE-registration-based technique may be more appropriate for contrast-enhanced T1-maps and/or for patients with dense regions of replacement fibrosis, such as those with ICM.

Efficacy of non-rigid registration technique for misregistration in 3D-CTA fusion imaging.

To assess whether fusion 3D-CTA images can be corrected using non-rigid registration (NRR) for gastroenterology imaging. This study included 55 patients before gastroenterology surgery who underwent preoperative 3D-CTA prior to gastroenterological surgery. We recorded the coordinate of measurement points on the arterial vessels (X, Y, and Z) in each portal phase, original image of the arterial phase, and arterial phase with NRR. The distance of misregistration between the two points was calculated with the coordinate of the original image with NRR and that of the portal phase as true value. The distance of misregistration between the two points in the original arterial and portal phase images was significantly higher than that in the arterial phase image with NRR on all directions (p < 0.01). This study showed that NRR may correct misregistration on fusion 3D-CTA imaging. Hence, it can visualize correctly the anatomy of the vessel.

Comparison and extension of three methods for automated registration of multimodal plant images

With the introduction of high-throughput multisensory imaging platforms, the automatization of multimodal image analysis has become the focus of quantitative plant research. Due to a number of natural and technical reasons (e.g., inhomogeneous scene illumination, shadows, and reflections), unsupervised identification of relevant plant structures (i.e., image segmentation) represents a nontrivial task that often requires extensive human-machine interaction. Registration of multimodal plant images enables the automatized segmentation of ’difficult’ image modalities such as visible light or near-infrared images using the segmentation results of image modalities that exhibit higher contrast between plant and background regions (such as fluorescent images). Furthermore, registration of different image modalities is essential for assessment of a consistent multiparametric plant phenotype, where, for example, chlorophyll and water content as well as disease- and/or stress-related pigmentation can simultaneously be studied at a local scale. To automatically register thousands of images, efficient algorithmic solutions for the unsupervised alignment of two structurally similar but, in general, nonidentical images are required. For establishment of image correspondences, different algorithmic approaches based on different image features have been proposed. The particularity of plant image analysis consists, however, of a large variability of shapes and colors of different plants measured at different developmental stages from different views. While adult plant shoots typically have a unique structure, young shoots may have a nonspecific shape that can often be hardly distinguished from the background structures. Consequently, it is not clear a priori what image features and registration techniques are suitable for the alignment of various multimodal plant images. Furthermore, dynamically measured plants may exhibit nonuniform movements that require application of nonrigid registration techniques. Here, we investigate three common techniques for registration of visible light and fluorescence images that rely on finding correspondences between (i) feature-points, (ii) frequency domain features, and (iii) image intensity information. The performance of registration methods is validated in terms of robustness and accuracy measured by a direct comparison with manually segmented images of different plants. Our experimental results show that all three techniques are sensitive to structural image distortions and require additional preprocessing steps including structural enhancement and characteristic scale selection. To overcome the limitations of conventional approaches, we develop an iterative algorithmic scheme, which allows it to perform both rigid and slightly nonrigid registration of high-throughput plant images in a fully automated manner.

Prediction of Liver Fibrosis Using CT Under Respiratory Control: New Attempt Using Deformation Vectors Obtained by Non-rigid Registration Technique.

To investigate whether liver fibrosis can be predicted by quantifying the deformity of the liver obtained based on computed tomographic (CT) images scanned under respiratory control. For dynamic CT of 47 patients, portal venous and equilibrium phases were scanned during inspiration and expiration, respectively. After rigid registration of the two images, non-rigid registration of the liver was performed, and the amount and direction of each voxel's shift during non-rigid registration was defined as the deformation vector. The correlation of each CT parameter for the obtained deformation vectors with the pathologically-proven degree of liver fibrosis was assessed using Spearman's rank correlation test. Receiver operating characteristic curve analysis was conducted for prediction of liver fibrosis. The standard deviation, coefficient of variance (CV) and skewness were significantly negatively correlated with the degree of liver fibrosis (p=0.030, 0.009 and 0.037, respectively). Of these measures, CV was best correlated and significantly decreased as liver fibrosis progressed (rho=-0.376). CV showed accuracies of 66.0-70.2%, and the areas under curves were 0.654-0.727 for prediction of fibrosis of grade F1 or greater, F2 or greater, F3 or greater and F4 fibrosis. The deformation vector is a potential CT parameter for evaluating liver fibrosis.

Racioethnic differences in the biomechanical response of the lamina cribrosa

Glaucoma is the second leading cause of irreversible blindness in the world with a higher prevalence in those of African Descent (AD) and Hispanic Ethnicity (HE) than in those of European Descent (ED). The objective of this study was to investigate the pressure dependent biomechanical response of the lamina cribrosa (LC) in normal human donor tissues from these racioethnic backgrounds. Pressure inflation tests were performed on 24 human LCs (n = 9 AD, n = 6 ED, and n = 9 HE) capturing the second harmonic generation (SHG) signal of collagen at 5, 15, 30, and 45 mmHg from an anterior view. A non-rigid image registration technique was utilized to determine the 3D displacement field in each LC from which 3D Green strains were calculated. The peak shear strain in the superior quadrant of the LC in those of ED was significantly higher than in those of AD and HE (p-value = 0.005 & 0.034, respectively) where ED = 0.017 [IQR = 0.012–0.027], AD = 0.0002 [IQR = −0.001–0.007], HE = 0.0016 [IQR = −0.002–0.012]). There were also significant differences in the regional strain heterogeneity in those of AD and HE that were absent in those of ED. This work represents, to our knowledge, the first ex-vivo study identifying significant differences in the biomechanical response of the LC in populations at increased risk of glaucoma. Future work will be necessary to assess if and how these differences play a role in predisposing those of Hispanic Ethnicity and African Descent to the onset and/or progression of primary open angle glaucoma. Statement of significanceGlaucoma is the second leading cause of irreversible blindness in the world and occurs more frequently in those of African Descent and Hispanic Ethnicity than in those of European Descent. To date, there has been no ex-vivo study quantifying differences in the biomechanical response of the non-glaucomatous lamina cribrosa (LC) across these racioethnic backgrounds. In this work we report, for the first time, differences in the pressure dependent biomechanical response of LC across different racioethnic groups as quantified using nonlinear optical microscopy. This study lays the foundation for future work investigating if and how these differences may play a role in predisposing those at increased risk to the onset and/or progression of primary open angle glaucoma.

Statistics and topology of local flame–flame interactions in turbulent flames

Flame–flame interaction events occur frequently in turbulent premixed flames and change the local structure and dynamics of flames. It is essential to understand these flame–flame interaction events to develop high-fidelity combustion models for use in modern combustion devices. In this study, we experimentally investigate the topology of flame–flame interaction events in single- and multi-flame configurations. A dual-burner experiment is probed with high-speed OH-planar laser-induced fluorescence and stereoscopic-particle image velocimetry to obtain simultaneous flame front locations and velocity fields. A non-rigid image registration technique is implemented to track the topological changes occurring in these flames. In both single- and dual-flame configurations, small-scale interactions occur more frequently compared to large-scale interactions, and statistics show that most of the reactant-side interactions contribute to large flame surface destructions than the product-side interactions. It is also found that turbulence length- and velocity-scales can play an important role in facilitating the interaction events and pocket formations from these events. Filamentarity is used to quantify the two-dimensional shape of these interactions and comparisons are made between the orientation and shape of interaction events and the local turbulence in the flowfield. Alignment between the orientation of the interaction shapes and the principal strain rates show that compressive fluid forces drive both types of interaction events.

Toward an Affine Feature-Based Registration Method for Ground Glass Lung Nodule Tracking.

Lung nodule progression assessment from medical imaging is a critical biomarker for assessing the course of the disease or the patient's response to therapy. CT images are routinely used to identify the location and size and rack the progression of lung nodules. However, nodule segmentation is challenging and prone to error, due to the irregular nodule boundaries, therefore introducing error in the lung nodule quantification process. Here, we describe the development and evaluation of a feature-based affine image registration framework that enables us to register two time point thoracic CT images as a means to account for the back-ground lung tissue deformation, then use digital subtraction images to assess tumor progression/regression. We have demonstrated this method on twelve de-identified patient datasets and showed that the proposed method yielded a better than 1.5mm registration accuracy vis-à-vis the widely accepted non-rigid image registration techniques. To demonstrate the potential clinical value of our described technique, we conducted a study in which our collaborating clinician was asked to provide an assessment of nodule progression/regression using the digital subtraction images post-registration. This assessment was consistent, yet provided more confidence, than the traditional lung nodule tracking based on visual analysis of the CT images.

Semi-autonomous Robotic Anastomoses of Vaginal Cuffs Using Marker Enhanced 3D Imaging and Path Planning.

Autonomous robotic anastomosis has the potential to improve surgical outcomes by performing more consistent suture spacing and bite size compared to manual anastomosis. However, due to soft tissue's irregular shape and unpredictable deformation, performing autonomous robotic anastomosis without continuous tissue detection and three-dimensional path planning strategies remains a challenging task. In this paper, we present a novel three-dimensional path planning algorithm for Smart Tissue Autonomous Robot (STAR) to enable semi-autonomous robotic anastomosis on deformable tissue. The algorithm incorporates (i) continuous detection of 3D near infrared (NIR) markers manually placed on deformable tissue before the procedure, (ii) generating a uniform and consistent suture placement plan using 3D path planning methods based on the locations of the NIR markers, and (iii) updating the remaining suture plan after each completed stitch using a non-rigid registration technique to account for tissue deformation during anastomosis. We evaluate the path planning algorithm for accuracy and consistency by comparing the anastomosis of synthetic vaginal cuff tissue completed by STAR and a surgeon. Our test results indicate that STAR using the proposed method achieves 2.6 times better consistency in suture spacing and 2.4 times better consistency in suture bite sizes than the manual anastomosis.

A Review on Registration of Medical Images Using Graph Theoretic Approaches

&lt;p&gt;Image registration is very essential for image analysis especially in the medical field. The registered image helps us to find a lot of things like the presence of a tumor, aneurysm, and many more. Image registration is a process of aligning two images that were captured during different conditions that makes analyzing of images possible. There are various methods in which images can be registered. This paper mainly discusses the methods that use the graph approach to register the medical images. The main focus is on rigid and non-rigid registration techniques and gives a brief description of the methods present.&lt;/p&gt;

The role of flow interaction in flame–flame interaction events in a dual burner experiment

A dual burner experiment is used to investigate how flow interactions affect local flame-flame interaction in turbulent premixed flames. The presence of adjacent flows influences the local structure of these flames and understanding the sensitivity of these flames to adjacent flows is essential for multi-nozzle combustion devices. To study this sensitivity, a high-aspect-ratio Bunsen flame operating at a constant flow velocity is placed adjacent to an identical burner with non-reacting flow at varying velocities. High-speed OH-planar laser induced fluorescence and stereoscopic-particle image velocimetry measurements are performed to capture flame-front locations and velocity fields. A non-rigid image registration technique is used to calculate the local flame-area variations that occur due to topological differences, and conditional statistics are extracted to relate the local behavior to changes observed in the global behavior of the flames. Extracted flame curvatures and time-averaged progress variables conditioned on flame-flame interactions show differences existing in the inner and outer flame branches near the flame-attachment region. Statistics of these results are presented and compared for all test cases.

Co-Sparse Analysis Model Based Image Registration to Compensate Brain Shift by Using Intra-Operative Ultrasound Imaging.

Notwithstanding the widespread use of image guided neurosurgery systems in recent years, the accuracy of these systems is strongly limited by the intra-operative deformation of the brain tissue, the so-called brain shift. Intra-operative ultrasound (iUS) imaging as an effective solution to compensate complex brain shift phenomena update patients coordinate during surgery by registration of the intra-operative ultrasound and the pre-operative MRI data that is a challenging problem.In this work a non-rigid multimodal image registration technique based on co-sparse analysis model is proposed. This model captures the interdependency of two image modalities; MRI as an intensity image and iUS as a depth image. Based on this model, the transformation between the two modalities is minimized by using a bimodal pair of analysis operators which are learned by optimizing a joint co-sparsity function using a conjugate gradient.Experimental validation of our algorithm confirms that our registration approach outperforms several of other state-of-the-art registration methods quantitatively. The evaluation was performed using seven patient dataset with the mean registration error of only 1.83 mm. Our intensity-based co-sparse analysis model has improved the accuracy of non-rigid multimodal medical image registration by 15.37% compared to the curvelet based residual complexity as a powerful registration method, in a computational time compatible with clinical use.