Retinal Fundus Image Registration via Vascular Structure Graph Matching

Motivated by the observation that a retinal fundus image may contain some unique geometric structures within its vascular trees which can be utilized for feature matching, in this paper, we proposed a graph-based registration framework called GM-ICP to align pairwise retinal images. First, the retinal vessels are automatically detected and represented as vascular structure graphs. A graph matching is then performed to find global correspondences between vascular bifurcations. Finally, a revised ICP algorithm incorporating with quadratic transformation model is used at fine level to register vessel shape models. In order to eliminate the incorrect matches from global correspondence set obtained via graph matching, we proposed a structure-based sample consensus (STRUCT-SAC) algorithm. The advantages of our approach are threefold: (1) global optimum solution can be achieved with graph matching; (2) our method is invariant to linear geometric transformations; and (3) heavy local feature descriptors are not required. The effectiveness of our method is demonstrated by the experiments with 48 pairs retinal images collected from clinical patients

KSS-ICP: Point Cloud Registration based on Kendall Shape Space

Point cloud registration is a popular topic which has been widely used in 3D model reconstruction, location, and retrieval. In this paper, we propose a new registration method, KSS-ICP, to address the rigid registration task in Kendall shape space (KSS) with Iterative Closest Point (ICP). The KSS is a quotient space that removes influences of translations, scales, and rotations for shape feature-based analysis. Such influences can be concluded as the similarity transformations that do not change the shape feature. The point cloud representation in KSS is invariant to similarity transformations. We utilize such property to design the KSS-ICP for point cloud registration. To tackle the difficulty to achieve the KSS representation in general, the proposed KSS-ICP formulates a practical solution that does not require complex feature analysis, data training, and optimization. With a simple implementation, KSS-ICP achieves more accurate registration from point clouds. It is robust to similarity transformation, non-uniform density, noise, and defective parts. Experiments show that KSS-ICP has better performance than the state of the art.Comment: 13 pages, 20 figure

Toward Global Localization of Unmanned Aircraft Systems using Overhead Image Registration with Deep Learning Convolutional Neural Networks

Global localization, in which an unmanned aircraft system (UAS) estimates its unknown current location without access to its take-off location or other locational data from its flight path, is a challenging problem. This research brings together aspects from the remote sensing, geoinformatics, and machine learning disciplines by framing the global localization problem as a geospatial image registration problem in which overhead aerial and satellite imagery serve as a proxy for UAS imagery. A literature review is conducted covering the use of deep learning convolutional neural networks (DLCNN) with global localization and other related geospatial imagery applications. Differences between geospatial imagery taken from the overhead perspective and terrestrial imagery are discussed, as well as difficulties in using geospatial overhead imagery for image registration due to a lack of suitable machine learning datasets. Geospatial analysis is conducted to identify suitable areas for future UAS imagery collection. One of these areas, Jerusalem northeast (JNE) is selected as the area of interest (AOI) for this research. Multi-modal, multi-temporal, and multi-resolution geospatial overhead imagery is aggregated from a variety of publicly available sources and processed to create a controlled image dataset called Jerusalem northeast rural controlled imagery (JNE RCI). JNE RCI is tested with handcrafted feature-based methods SURF and SIFT and a non-handcrafted feature-based pre-trained fine-tuned VGG-16 DLCNN on coarse-grained image registration. Both handcrafted and non-handcrafted feature based methods had difficulty with the coarse-grained registration process. The format of JNE RCI is determined to be unsuitable for the coarse-grained registration process with DLCNNs and the process to create a new supervised machine learning dataset, Jerusalem northeast machine learning (JNE ML) is covered in detail. A multi-resolution grid based approach is used, where each grid cell ID is treated as the supervised training label for that respective resolution. Pre-trained fine-tuned VGG-16 DLCNNs, two custom architecture two-channel DLCNNs, and a custom chain DLCNN are trained on JNE ML for each spatial resolution of subimages in the dataset. All DLCNNs used could more accurately coarsely register the JNE ML subimages compared to the pre-trained fine-tuned VGG-16 DLCNN on JNE RCI. This shows the process for creating JNE ML is valid and is suitable for using machine learning with the coarse-grained registration problem. All custom architecture two-channel DLCNNs and the custom chain DLCNN were able to more accurately coarsely register the JNE ML subimages compared to the fine-tuned pre-trained VGG-16 approach. Both the two-channel custom DLCNNs and the chain DLCNN were able to generalize well to new imagery that these networks had not previously trained on. Through the contributions of this research, a foundation is laid for future work to be conducted on the UAS global localization problem within the rural forested JNE AOI

Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers

In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers

Characterising brain connectivity along the lifespan in a rodent model of healthy ageing

The brain parenchyma undergoes several structural changes throughout life, which have a ma- jor impact on its physiological evolution, and which are behaviorally reflected as changes in cognition and ability. A key question is how age-related structural alterations impact the func- tion of the different areas. Functional connectivity, measured as correlation between brain re- gions during the resting state Magnetic Resonance Imaging (MRI), is a quantitative measure of function that can be reliably used to characterize the evolution of the communication between regions across the lifespan. However, most of the works so far have done it with a hypothesis driven approach. The present work aims to identify the functional connectivity patterns of the whole brain during resting state in a rodent model of healthy ageing. For this purpose, we have followed the standard workflow recently proposed in a consensus paper on functional imag- ing processing in preclinical MRI. We have set up a longitudinal functional MRI experiment to measure functional connectivity in rats at different times. Independent component analysis has been used to identify characteristic resting-state networks and compare them between three different ages, corresponding to adulthood to early senescence. The goal is to highlight region- , sex-, and age-specific patterns that drive the physiological decline in cognition observed in senescence, with potential to identify vulnerable regions in and define targets for intervention. Our results uncovered patterns of increased functional connectivity between adulthood and senescence in several key regions controlling the functions known to be affected by age. Such increase in connectivity can be explained as a compensatory mechanism that allows the brain to cope with reduced microstructural integrity. The study of healthy ageing in absence of disease sets the baseline for the identification of pathological conditionsEl parénquima cerebral experimenta varios cambios estructurales a lo largo de la vida, que tienen un gran impacto en su evolución fisiológica, y que se reflejan conductualmente como cambios en la cognición y la capacidad. Una cuestión clave es cómo repercuten las alteraciones estructurales relacionadas con la edad en la función de las distintas áreas. La conectividad fun- cional, medida como correlación entre regiones cerebrales durante la Resonancia Magnética (RM) en estado de reposo, es una medida cuantitativa de la función que puede utilizarse de forma fiable para caracterizar la evolución de la comunicación entre regiones a lo largo de la vida. Sin embargo, la mayoría de los trabajos realizados hasta ahora lo han hecho con un en- foque basado en hipótesis. El presente trabajo pretende identificar los patrones de conectividad funcional de todo el cerebro durante el estado de reposo en un modelo de roedor de envejec- imiento sano. Para ello, hemos seguido el flujo de trabajo estándar propuesto recientemente en un documento de consenso sobre el procesamiento de imágenes funcionales en RM preclínica. Hemos establecido un experimento de RM funcional longitudinal para medir la conectividad funcional en ratas en diferentes momentos. Se ha utilizado el análisis de componentes indepen- dientes para identificar redes características en estado de reposo y compararlas entre tres edades diferentes, correspondientes a la edad adulta y a la senescencia temprana. El objetivo es destacar los patrones específicos de región, sexo y edad que impulsan el declive fisiológico de la cogni- ción observado en la senescencia, con potencial para identificar regiones vulnerables y definir objetivos de intervención. Nuestros resultados descubrieron patrones de aumento de la conec- tividad funcional entre la edad adulta y la senescencia en varias regiones clave que controlan las funciones que se sabe que se ven afectadas por la edad. Este aumento de la conectividad puede explicarse como un mecanismo compensatorio que permite al cerebro hacer frente a la reducción de la integridad microestructural. El estudio del envejecimiento sano en ausencia de enfermedad sienta las bases para la identificación de condiciones patológicasEl parènquima cerebral experimenta diversos canvis estructurals al llarg de la vida, que tenen un gran impacte en la seua evolució fisiològica, i que es reflecteixen conductualment com a canvis en la cognició i la capacitat. Una qüestió clau és com repercuteixen les alteracions estructurals relacionades amb l’edat en la funció de les diferents àrees. La connectivitat funcional, mesurada com a correlació entre regions cerebrals durant la Ressonància Magnètica (RM) en estat de repòs, és una mesura quantitativa de la funció que pot utilitzar-se de manera fiable per a carac- teritzar l’evolució de la comunicació entre regions al llarg de la vida. No obstant això, la majoria dels treballs realitzats fins ara ho han fet amb un enfocament basat en hipòtesi. El present tre- ball pretén identificar els patrons de connectivitat funcional de tot el cervell durant l’estat de repòs en un model de rosegador d’envelliment sa. Per a això, hem seguit el flux de treball estàndard proposat recentment en un document de consens sobre el processament d’imatges funcionals en RM preclínica. Hem establit un experiment de RM funcional longitudinal per a mesurar la connectivitat funcional en rates en diferents moments. S’ha utilitzat l’anàlisi de com- ponents independents per a identificar xarxes característiques en estat de repòs i comparar-les entre tres edats diferents, corresponents a l’edat adulta i a la senescència primerenca. L’objectiu és destacar els patrons específics de regió, sexe i edat que impulsen el declivi fisiològic de la cognició observat en la senescència, amb potencial per a identificar regions vulnerables i definir objectius d’intervenció. Els nostres resultats van descobrir patrons d’augment de la connec- tivitat funcional entre l’edat adulta i la senescència en diverses regions clau que controlen les funcions que se sap que es veuen afectades per l’edat. Aquest augment de la connectivitat pot explicar-se com un mecanisme compensatori que permet al cervell fer front a la reducció de la integritat microestructural. L’estudi de l’envelliment sa en absència de malaltia estableix les bases per a la identificació de condicions patològique

Streaming Monte Carlo Pose Estimation for Autonomous Object Modeling

This work contributes the optimization of a streaming pose estimation particle filter and its integration into an autonomous object modeling approach. The particle filter is advanced by an additional pose optimization in the particle weighting step. By integrating the method into the autonomous object modeling approach, the repositioning of objects is enabled, which is often necessary in order to acquire complete models. Experiments show that the usage of iterative closest point is too restrictive for general transformations. The used Monte Carlo method enables a robust pose estimation without loss of time and with high precision. Further, it is shown that the overall modeling results are improved clearly

Object Tracking

Object tracking consists in estimation of trajectory of moving objects in the sequence of images. Automation of the computer object tracking is a difficult task. Dynamics of multiple parameters changes representing features and motion of the objects, and temporary partial or full occlusion of the tracked objects have to be considered. This monograph presents the development of object tracking algorithms, methods and systems. Both, state of the art of object tracking methods and also the new trends in research are described in this book. Fourteen chapters are split into two sections. Section 1 presents new theoretical ideas whereas Section 2 presents real-life applications. Despite the variety of topics contained in this monograph it constitutes a consisted knowledge in the field of computer object tracking. The intention of editor was to follow up the very quick progress in the developing of methods as well as extension of the application

Repeatability of perfusion measurements in adult gliomas using pulsed and pseudo-continuous arterial spin labelling MRI

Objectives: To investigate the repeatability of perfusion measures in gliomas using pulsed- and pseudo-continuous-arterial spin labelling (PASL, PCASL) techniques, and evaluate different regions-of-interest (ROIs) for relative tumour blood flow (rTBF) normalisation. / Materials and methods: Repeatability of cerebral blood flow (CBF) was measured in the Contralateral Normal Appearing Hemisphere (CNAH) and in brain tumours (aTBF). rTBF was normalised using both large/small ROIs from the CNAH. Repeatability was evaluated with intra-class-correlation-coefficient (ICC), Within-Coefficient-of-Variation (WCoV) and Coefficient-of-Repeatability (CR). / Results: PASL and PCASL demonstrated high reliability (ICC > 0.9) for CNAH-CBF, aTBF and rTBF. PCASL demonstrated a more stable signal-to-noise ratio (SNR) with a lower WCoV of the SNR than that of PASL (10.9–42.5% vs. 12.3–29.2%). PASL and PCASL showed higher WCoV in aTBF and rTBF than in CNAH CBF in WM and GM but not in the caudate, and higher WCoV for rTBF than for aTBF when normalised using a small ROI (PASL 8.1% vs. 4.7%, PCASL 10.9% vs. 7.9%, respectively). The lowest CR was observed for rTBF normalised with a large ROI. / Discussion: PASL and PCASL showed similar repeatability for the assessment of perfusion parameters in patients with primary brain tumours as previous studies based on volunteers. Both methods displayed reasonable WCoV in the tumour area and CNAH. PCASL’s more stable SNR in small areas (caudate) is likely to be due to the longer post-labelling delays