Map Registration Research Articles

SP-0586: Role, clinical application and validation of deformable image registration and dose mapping tools

SP-0586: Role, clinical application and validation of deformable image registration and dose mapping tools

Secure binary arithmetic coding based on digitalized modified logistic map and linear feedback shift register

In this paper, we propose a novel secure arithmetic coding based on digitalized modified logistic map (DMLM) and linear feedback shift register (LFSR). An input binary sequence is first mapped into a table, which is then scrambled by two cyclic shift steps driven by the keys resulting from DMLM–LFSR. Next, each column is encoded using traditional arithmetic coding (TAC) and randomized arithmetic coding (RAC). During the RAC process, the exchange of two intervals is controlled by the keystream generated from the DMLM. At the same time, a few bits of the present column sequence are extracted to interfere the generation of new keystream used for the next column. The final ciphertext sequence is obtained by XORing the compressed sequence and the keystream generated by the LFSR. Results show the compression ratio of our scheme is slightly higher than that of TAC, but the security is improved due to the architecture of shift–perturbance. DMLM and LFSR theories also ensure high sensitivity and strong randomness. The appended complexity is only O(N), where N is the number of the input symbols.

Patterns of distribution of childhood cancer in Africa.

Very little is known about the regional variation in the incidence of childhood malignancies in Africa. The aim of the study was to perform a comprehensive analysis of the distribution of childhood cancer in Sub-Saharan Africa and compare the results to the Globocan estimations. A letter of invitation to participate was sent to all registry centers in Africa registered with the International Agency for Research on Cancer and to all African centers registered with AORTIC and SIOP Africa, requesting similar information as in CanReg 4. Childhood cancers were defined as those occurring below the age of 15 years. The data requested was from 2000 to 2010. The malignancies were classified and coded according to the International Classification of Childhood Cancer, 2004 system. Data obtained were analyzed using EpiInfo and Statistica 10 software. Information regarding the estimation of the numbers and incidence of the top 5 childhood cancers in specific countries was obtained from Globocan Web site. There were 21 centers included in the study from 18 Sub-Saharan African countries. The data analyzed differed from center to center and included cases from 1985 to 2011. The proportion of childhood cancer out of all cancers ranged between 1.4% in Ghana to 10.0% in Rwanda. In Southern Africa, Kaposi sarcoma was the most common malignancy in children in Mozambique (15.8% of all cases) and the second most common in Zambia (15.6%) and in Malawi (12.4%). In Eastern Africa, Uganda recorded Kaposi sarcoma as the most common tumor in children (22.0%), while two Kenyan centers reported mainly Burkitt lymphoma (25.1 and 37.1%, respectively). In Central Africa, Congo classified retinoblastoma as the most common childhood cancer with an incidence of 20.1%. In Western Africa, Non-Hodgkin lymphoma was the most common in Ghana (53.6%), in Ivory Coast (73.6%) and in Mali (32.7%). Nephroblastoma remains the most common solid tumor in Africa exceeding 10% of total pediatric cancers in many countries (Rwanda 21.3%, Senegal 22%, Ivory Coast 14.5%, Mali 17.6%, Congo 15.5%, etc). Unlike developed countries, lymphomas, nephroblastoma, Kaposi sarcoma and retinoblastoma were the most common pediatric tumors in Africa. Globocan estimations despite bringing significant contribution to the registration map cannot replace the data from local hospital and population-based registries. All efforts should be directed in developing functional and reliable childhood cancer registries across the African continent.

Effect of image registration on longitudinal analysis of retinal nerve fiber layer thickness of non-human primates using Optical Coherence Tomography (OCT).

BackgroundIn this paper we determined the benefits of image registration on estimating longitudinal retinal nerve fiber layer thickness (RNFLT) changes.MethodsRNFLT maps around the optic nerve head (ONH) of healthy primate eyes were measured using Optical Coherence Tomography (OCT) weekly for 30 weeks. One automatic algorithm based on mutual information (MI) and the other semi-automatic algorithm based on log-polar transform cross-correlation using manually segmented blood vessels (LPCC_MSBV), were used to register retinal maps longitudinally. We compared the precision and recall between manually segmented image pairs for the two algorithms using a linear mixed effects model.ResultsWe found that the precision calculated between manually segmented image pairs following registration by LPCC_MSBV algorithm is significantly better than the one following registration by MI algorithm (p < <0.0001). Trend of the all-rings and temporal, superior, nasal and inferior (TSNI) quadrants average of RNFLT over time in healthy primate eyes are not affected by registration. RNFLT of clock hours 1, 2, and 10 showed significant change over 30 weeks (p = 0.0058, 0.0054, and 0.0298 for clock hours 1, 2 and 10 respectively) without registration, but stayed constant over time with registration.ConclusionsThe LPCC_MSBV provides better registration of RNFLT maps recorded on different dates than the automatic MI algorithm. Registration of RNFLT maps can improve clinical analysis of glaucoma progression.Electronic supplementary materialThe online version of this article (doi:10.1186/s40662-015-0013-7) contains supplementary material, which is available to authorized users.

Beyond technology: Japanese colonial mapping of water estates in Taiwan from 1901 to 1921

ABSTRACTIrrigation systems in Taiwan were originally developed as private property and first put under strict government control during the Japanese colonial rule from 1895 to 1945. To understand how the Japanese introduced mapping to facilitate their early governance on irrigation systems developed by locals, this study explored the irrigation reform implemented in 1901 and its influence on the emergence of two types of irrigation mapping. One is entitled ‘Irrigation Register Maps’ and the other ‘Irrigation Application Maps’. By exploring unpublished papers in the Archives of the Government-General of Taiwan (Taiwan Sotokufu), the manuscripts of these two types of irrigation map were examined in terms of their mapping initiatives, rationales and techniques. While ‘Irrigation Register Maps’ were produced by the colonial government to illustrate the existing water ownership with public interest, ‘Irrigation Application Maps’ were written by the local residents in order to claim new water ownership with private interest. In addition to depicting irrigation routes, both types of mapping were integrated with rich textual information about water ownership and management frameworks. To conclude, this paper argues that Japanese colonial mapping was an in-depth activity about re-presenting and reshaping local cultures, instead of merely reproducing Western technologies.

일제강점기 천안군 중심부 필지구조의 복원적 고찰 : 조선후기 천안군 읍치시설 비정을 위하여

To understand the governing institutions` arrangement in an old city of the late Joseon dynasty, it is necessary to compare and weigh the detailed old map with regional geographical records Eupji. In particular, the investigation of `Closure Land Registration Map` is indispensable, which was made in the period of the Japanese occupation. This study aims to restore the urban land lots arrangement of Cheonan-Gun`s old center part in the initial period of the Japanese occupation, using the oldest `Closure Land Registration Map(1940)` of Cheonan-Gun. The results of this study will be helpful in understanding the governing institutions` arrangement of Cheonan-Gun in the late Joseon dynasty.

Image-based motion correction for optical mapping of cardiac electrical activity.

Optical mapping, with membrane-bound, voltage-sensitive dyes, is widely used for in vitro recording of cardiac electrical activity. The spatial registration of such maps is lost when the heart moves with respect to a fixed photodetector array and contraction can generate substantial artifact if background fluorescence is not uniformly distributed. While motion artifact is commonly suppressed with electromechanical uncoupling agents, there are circumstances where these are undesirable. This study outlines a novel image-based approach for retrospective motion artifact correction. Isolated Langendorff-supported rat hearts (n=8), stained with di-4-ANEPPS, were illuminated at 516±14nm and fluorescent emission (>565±10nm) was acquired with a charge multiplying CCD camera. Background fluorescence was segmented in successive frames and stabilized using a non-rigid image registration algorithm. The resultant image deformation was used to estimate material point movement on the heart surface, so that total fluorescence could be mapped frame-by-frame to appropriate reference pixels. Finally, residual motion artifact was identified and removed. The effectiveness of this correction method was evaluated over 18 experimental datasets. Signal-to-noise ratio was increased more than fourfold, and activation time and action potential duration (APD) could be estimated at 24% more pixels than in the raw data. The variability of all APD measures was substantially reduced (i.e. APD50 estimated as 83.8±45.8ms before correction was 52.1±4.7ms afterward). This approach provides a robust means of recovering optical action potentials in the presence of substantial motion artifact.

Left-hemispheric microstructural abnormalities in children with high-functioning autism spectrum disorder.

Current theories of the neurobiological basis of autism spectrum disorder (ASD) posit an altered pattern of connectivity in large-scale brain networks. Here we used diffusion tensor imaging to investigate the microstructural properties of the white matter (WM) that mediates interregional connectivity in 36 high-functioning children with ASD (HF-ASD) as compared with 37 controls. By employing an atlas-based analysis using large deformation diffeometric morphic mapping registration, a widespread but left-lateralized pattern of abnormalities was revealed. The mean diffusivity (MD) of water in the WM of HF-ASD children was significantly elevated throughout the left hemisphere, particularly in the outer-zone cortical WM. Across diagnostic groups, there was a significant effect of age on left-hemisphere MD, with a similar reduction in MD during childhood in both typically developing and HF-ASD children. The increased MD in children with HF-ASD suggests hypomyelination and may reflect increased short-range cortico-cortical connections subsequent to early WM overgrowth. These findings also highlight left-hemispheric connectivity as relevant to the pathophysiology of ASD and indicate that the spatial distribution of microstructural abnormalities in HF-ASD is widespread and left-lateralized. This altered left-hemispheric connectivity may contribute to deficits in communication and praxis observed in ASD.

A fast and mobile system for registration of low-altitude visual and thermal aerial images using multiple small-scale UAVs

The use of multiple small-scale UAVs to support first responders in disaster management has become popular because of their speed and low deployment costs. We exploit such UAVs to perform real-time monitoring of target areas by fusing individual images captured from heterogeneous aerial sensors. Many approaches have already been presented to register images from homogeneous sensors. These methods have demonstrated robustness against scale, rotation and illumination variations and can also cope with limited overlap among individual images.In this paper we focus on thermal and visual image registration and propose different methods to improve the quality of interspectral registration for the purpose of real-time monitoring and mobile mapping. Images captured by low-altitude UAVs represent a very challenging scenario for interspectral registration due to the strong variations in overlap, scale, rotation, point of view and structure of such scenes. Furthermore, these small-scale UAVs have limited processing and communication power. The contributions of this paper include (i) the introduction of a feature descriptor for robustly identifying corresponding regions of images in different spectrums, (ii) the registration of image mosaics, and (iii) the registration of depth maps. We evaluated the first method using a test data set consisting of 84 image pairs. In all instances our approach combined with SIFT or SURF feature-based registration was superior to the standard versions. Although we focus mainly on aerial imagery, our evaluation shows that the presented approach would also be beneficial in other scenarios such as surveillance and human detection. Furthermore, we demonstrated the advantages of the other two methods in case of multiple image pairs.

Unified voxel- and tensor-based morphometry (UVTBM) using registration confidence

Voxel-based morphometry (VBM) and tensor-based morphometry (TBM) both rely on spatial normalization to a template and yet have different requirements for the level of registration accuracy. VBM requires only global alignment of brain structures, with limited degrees of freedom in transformation, whereas TBM performs best when the registration is highly deformable and can achieve higher registration accuracy. In addition, the registration accuracy varies over the whole brain, with higher accuracy typically observed in subcortical areas and lower accuracy seen in cortical areas. Hence, even the determinant of Jacobian of registration maps is spatially varying in their accuracy, and combining these with VBM by direct multiplication introduces errors in VBM maps where the registration is inaccurate. We propose a unified approach to combining these 2 morphometry methods that is motivated by these differing requirements for registration and our interest in harnessing the advantages of both. Our novel method uses local estimates of registration confidence to determine how to weight the influence of VBM- and TBM-like approaches. Results are shown on healthy and mild Alzheimer's subjects (N = 150) investigating age and group differences, and potential of differential diagnosis is shown on a set of Alzheimer's disease (N = 34) and frontotemporal dementia (N = 30) patients compared against controls (N = 14). These show that the group differences detected by our proposed approach are more descriptive than those detected from VBM, Jacobian-modulated VBM, and TBM separately, hence leveraging the advantages of both approaches in a unified framework.

Rough Terrain 3D Mapping and Navigation Using a Continuously Rotating 2D Laser Scanner

Mapping, real-time localization, and path planning are prerequisites for autonomous robot navigation. These functions also facilitate situation awareness of remote operators. In this paper, we propose methods for efficient 3D mapping and real-time 6D pose tracking of autonomous robots using a continuously rotating 2D laser scanner. We have developed our approach in the context of the DLR SpaceBot Cup robotics challenge. Multi-resolution surfel representations allow for compact maps and efficient registration of local maps. Real-time pose tracking is performed by a particle filter observing individual laser scan lines. Terrain drivability is assessed within a global environment map and used for planning feasible paths. Our approach is evaluated using challenging real environments.

Global Localization from Monocular SLAM on a Mobile Phone

We propose the combination of a keyframe-based monocular SLAM system and a global localization method. The SLAM system runs locally on a camera-equipped mobile client and provides continuous, relative 6DoF pose estimation as well as keyframe images with computed camera locations. As the local map expands, a server process localizes the keyframes with a pre-made, globally-registered map and returns the global registration correction to the mobile client. The localization result is updated each time a keyframe is added, and observations of global anchor points are added to the client-side bundle adjustment process to further refine the SLAM map registration and limit drift. The end result is a 6DoF tracking and mapping system which provides globally registered tracking in real-time on a mobile device, overcomes the difficulties of localization with a narrow field-of-view mobile phone camera, and is not limited to tracking only in areas covered by the offline reconstruction.

Development of Web Services for WLAN-based Indoor Positioning and Floor Map Repositories

Indoor location based service (ILBS) is one of the current hottest research issues. ILBS cannot be realized without indoor positioning solutions. Among the many existing indoor positioning methods, the Wireless Local Area Network (WLAN) based trilateration is one of the most economic and easiest methods to be installed. The WLAN-based trilateration estimates the distance between an access point (AP) and a mobile device with the signal strength of the signal from the AP. Then, the WLAN-based trilateration estimates the location of the mobile device with the estimated distances and the coordinates of the APs. The first contribution of this paper is to propose a floor map repository that allows users to upload floor map files (AutoCAD files) and register coordinates of APs so that any software developer can use them after getting authorization. One of the drawbacks of the trilateration is its inaccuracy. The RF (radio frequency) signals between APs and a mobile device are very sensitive to the noise and the estimated distances are very inaccurate. The second contribution of this paper is that after discussing the pros and cons of various modified versions of trilateration, we find our own solution for indoor positioning and implement and publish our solution as a web service so that any software developer can use it.

Registration of vector maps based on multiple geometric features in topological aspects

Maps are one of the most intuitive tools for recording and representing geographic information. Digital maps composed of vectors are mostly used for preserving cartographic data in a compact and efficient manner. However, the registration of multiple vector maps is an essential step when geographic information from various sources is used in an integrated analysis. Typically, control points are used as observables to solve the transformation parameters of a registration model, while check points are used for quality assessment. As a consequence, poor distribution or an insufficient number of control or check points might lead to a biased registration solution. In this study, linear features and projective invariant points were both used as observables in a registration analysis between multiple vector maps. The goal was to provide an improved geometric constraint when solving the transformation parameters. Furthermore, two numerical indices, namely the absolute consistency and relative geometric similarity, were used to evaluate the quality of the registration solution. Based on the results of a case study, the proposed approach has been shown to be capable of better making use of the geometric connotation inherent in a vector map and providing a comprehensive quality evaluation for the obtained transformation solution. Consequently, a more reliable and robust registration analysis for digital vector maps can be achieved when the proposed approach is implemented.

A Joint Land Cover Mapping and Image Registration Algorithm Based on a Markov Random Field Model

Traditionally, image registration of multi-modal and multi-temporal images is performed satisfactorily before land cover mapping. However, since multi-modal and multi-temporal images are likely to be obtained from different satellite platforms and/or acquired at different times, perfect alignment is very difficult to achieve. As a result, a proper land cover mapping algorithm must be able to correct registration errors as well as perform an accurate classification. In this paper, we propose a joint classification and registration technique based on a Markov random field (MRF) model to simultaneously align two or more images and obtain a land cover map (LCM) of the scene. The expectation maximization (EM) algorithm is employed to solve the joint image classification and registration problem by iteratively estimating the map parameters and approximate posterior probabilities. Then, the maximum a posteriori (MAP) criterion is used to produce an optimum land cover map. We conducted experiments on a set of four simulated images and one pair of remotely sensed images to investigate the effectiveness and robustness of the proposed algorithm. Our results show that, with proper selection of a critical MRF parameter, the resulting LCMs derived from an unregistered image pair can achieve an accuracy that is as high as when images are perfectly aligned. Furthermore, the registration error can be greatly reduced.

필지 객체의 형상 정합을 이용한 건물 설계도면의 좌표 등록

This study proposed a map registration method of a building construction plan drawing with shape matching of cadastral parcel polygon. In general, the drawing contains information about a building boundary and a cadastral parcel boundary. The shape of this cadastral parcel boundary should be same as that of the corresponding parcel polygon object in the KLIS continuous cadastral map. Thus, shape matching between two parcel boundary polygons from the drawing and cadastral map could present transformation parameters. Translation and scaling amounts could be obtained by difference of centroid coordinates and area ratio of the polygons, respectively. Rotation amount could be obtained by the rotation that presents the minimum Turning function dissimilarity of the polygons. The proposed method was applied for building construction plan drawings in eAIS for an urban area in Suwon. To assess positional accuracy of map registration, building polygons in registered drawings and aerial photos were compared. According to the accuracy of the cadastral map which is the reference dataset of the proposed method, the RMSE of corresponding buildings` corners was 0.95m and 2.37m in new and old urban areas, respectively.

Mapping registration sensitivity in MR mouse brain images

Nonlinear registration algorithms provide a way to estimate structural (brain) differences based on magnetic resonance images. Their ability to align images of different individuals and across modalities has been well-researched, but the bounds of their sensitivity with respect to the recovery of salient morphological differences between groups are unclear. Here we develop a novel approach to simulate deformations on MR brain images to evaluate the ability of two registration algorithms to extract structural differences corresponding to biologically plausible atrophy and expansion. We show that at a neuroanatomical level registration accuracy is influenced by the size and compactness of structures, but do so differently depending on how much change is simulated. The size of structures has a small influence on the recovered accuracy. There is a trend for larger structures to be recovered more accurately, which becomes only significant as the amount of simulated change is large. More compact structures can be recovered more accurately regardless of the amount of simulated change. Both tested algorithms underestimate the full extent of the simulated atrophy and expansion. Finally we show that when multiple comparisons are corrected for at a voxelwise level, a very low rate of false positives is obtained. More interesting is that true positive rates average around 40%, indicating that the simulated changes are not fully recovered. Simulation experiments were run using two fundamentally different registration algorithms and we identified the same results, suggesting that our findings are generalizable across different classes of nonlinear registration algorithms.

On the development of a robust, fast and lightweight keypoint descriptor

In this paper we introduce BRAND—Binary Robust Appearance and Normal Descriptor, a novel descriptor which efficiently combines appearance and geometric information from RGB-D images, that is largely invariant to rotation and scale transformations. Based on relevant characteristics of successful image only descriptors, we define a set of eight fundamental requirements to guide the design and evaluation of descriptors that also use depth information. We then describe the design of BRAND, followed by the evaluation of its performance according to those requirements. We also show how BRAND can be simplified in order to obtain a higher performance version, that we named BASE, for applications that require speed performance, but do not demand rigorous scale and rotation invariance.We compare the performance of BRAND against three standard descriptors on real world data. Results of several experiments demonstrate that as far as precision and robustness is concerned, BRAND compares favorably to SIFT and SURF for textured images, and to Spin-Image, for geometrical shape information. Furthermore, BRAND attains improved results when compared to state of the art descriptors that are based either on texture or geometry alone, or on their combination.Finally, we report on the use of BRAND in two applications for which we show that it provides reliable results for the registration of indoor textured depth maps and for object recognition in tasks that require the extraction of semantic knowledge.

Computational analysis of LDDMM for brain mapping.

One goal of computational anatomy (CA) is to develop tools to accurately segment brain structures in healthy and diseased subjects. In this paper, we examine the performance and complexity of such segmentation in the framework of the large deformation diffeomorphic metric mapping (LDDMM) registration method with reference to atlases and parameters. First we report the application of a multi-atlas segmentation approach to define basal ganglia structures in healthy and diseased kids' brains. The segmentation accuracy of the multi-atlas approach is compared with the single atlas LDDMM implementation and two state-of-the-art segmentation algorithms—Freesurfer and FSL—by computing the overlap errors between automatic and manual segmentations of the six basal ganglia nuclei in healthy subjects as well as subjects with diseases including ADHD and Autism. The high accuracy of multi-atlas segmentation is obtained at the cost of increasing the computational complexity because of the calculations necessary between the atlases and a subject. Second, we examine the effect of parameters on total LDDMM computation time and segmentation accuracy for basal ganglia structures. Single atlas LDDMM method is used to automatically segment the structures in a population of 16 subjects using different sets of parameters. The results show that a cascade approach and using fewer time steps can reduce computational complexity as much as five times while maintaining reliable segmentations.

Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

To achieve sparse parametrizations that allow intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that have the description capacity to represent the deformation compactly. To accomplish this, we introduce in this paper higher-order momentum distributions in the large deformation diffeomorphic metric mapping (LDDMM) registration framework. While the zeroth-order moments previously used in LDDMM only describe local displacement, the first-order momenta that are proposed here represent a basis that allows local description of affine transformations and subsequent compact description of nontranslational movement in a globally nonrigid deformation. The resulting representation contains directly interpretable information from both mathematical and modeling perspectives. We develop the mathematical construction of the registration framework with higher-order momenta, we show the implications for sparse image registration and deformation description, and we provide examples of how the parametrization enables registration with a very low number of parameters. The capacity and interpretability of the parametrization using higher-order momenta lead to natural modeling of articulated movement, and the method promises to be useful for quantifying ventricle expansion and progressing atrophy during Alzheimer's disease.