Methods for three-dimensional Registration of Multimodal Abdominal Image Data

Multimodal image registration benefits the diagnosis, treatment planning and the performance of image-guided procedures in the liver, since it enables the fusion of complementary information provided by pre- and intrainterventional data about tumor localization and access. Although there exist various registration methods, approaches which are specifically optimized for the registration of multimodal abdominal scans are only scarcely available. The work presented in this thesis aims to tackle this problem by focusing on the development, optimization and evaluation of registration methods specifically for the registration of multimodal liver scans. The contributions to the research field of medical image registration include the development of a registration evaluation methodology that enables the comparison and optimization of linear and non-linear registration algorithms using a point-based accuracy measure. This methodology has been used to benchmark standard registration methods as well as novel approaches that were developed within the frame of this thesis. The results of the methodology showed that the employed similarity measure used during the registration has a major impact on the registration accuracy of the method. Due to this influence, two alternative similarity metrics bearing the potential to be used on multimodal image data are proposed and evaluated. The first metric relies on the use of gradient information in form of Histograms of Oriented Gradients (HOG) whereas the second metric employs a siamese neural network to learn a similarity measure directly on the image data. The evaluation showed, that both metrics could compete with state of the art similarity measures in terms of registration accuracy. The HOG-metric offers the advantage that it does not require ground truth data to learn a similarity estimation, but instead it is applicable to various data sets with the sole requirement of distinct gradients. However, the Siamese metric is characterized by a higher robustness for large rotations than the HOG-metric. To train such a network, registered ground truth data is required which may be critical for multimodal image data. Yet, the results show that it is possible to apply models trained on registered synthetic data on real patient data. The last part of this thesis focuses on methods to learn an entire registration process using neural networks, thereby offering the advantage to replace the traditional, time-consuming iterative registration procedure. Within the frame of this thesis, the so-called VoxelMorph network which was originally proposed for monomodal, non-linear registration learning is extended for affine and multimodal registration learning tasks. This extension includes the consideration of an image mask during metric evaluation as well as loss functions for multimodal data, such as the pretrained Siamese metric and a loss relying on the comparison of deformation fields. Based on the developed registration evaluation methodology, the performance of the original network as well as the extended variants are evaluated for monomodal and multimodal registration tasks using multiple data sets. With the extended network variants, it is possible to learn an entire multimodal registration process for the correction of large image displacements. As for the Siamese metric, the results imply a general transferability of models trained with synthetic data to registration tasks including real patient data. Due to the lack of multimodal ground truth data, this transfer represents an important step towards making Deep Learning based registration procedures clinically usable

Fingerprint Matching using A Hybrid Shape and Orientation Descriptor

From the privacy perspective most concerns arise from the storage and misuse of biometric data (Cimato et al., 2009). ... is provided with a in-depth discussion of the state-of-the-art in iris biometric cryptosystems, which completes this work

Learning to Generate and Refine Object Proposals

Visual object recognition is a fundamental and challenging problem in computer vision. To build a practical recognition system, one is first confronted with high computation complexity due to an enormous search space from an image, which is caused by large variations in object appearance, pose and mutual occlusion, as well as other environmental factors. To reduce the search complexity, a moderate set of image regions that are likely to contain an object, regardless of its category, are usually first generated in modern object recognition subsystems. These possible object regions are called object proposals, object hypotheses or object candidates, which can be used for down-stream classification or global reasoning in many different vision tasks like object detection, segmentation and tracking, etc. This thesis addresses the problem of object proposal generation, including bounding box and segment proposal generation, in real-world scenarios. In particular, we investigate the representation learning in object proposal generation with 3D cues and contextual information, aiming to propose higher-quality object candidates which have higher object recall, better boundary coverage and lower number. We focus on three main issues: 1) how can we incorporate additional geometric and high-level semantic context information into the proposal generation for stereo images? 2) how do we generate object segment proposals for stereo images with learning representations and learning grouping process? and 3) how can we learn a context-driven representation to refine segment proposals efficiently? In this thesis, we propose a series of solutions to address each of the raised problems. We first propose a semantic context and depth-aware object proposal generation method. We design a set of new cues to encode the objectness, and then train an efficient random forest classifier to re-rank the initial proposals and linear regressors to fine-tune their locations. Next, we extend the task to the segment proposal generation in the same setting and develop a learning-based segment proposal generation method for stereo images. Our method makes use of learned deep features and designed geometric features to represent a region and learns a similarity network to guide the superpixel grouping process. We also learn a ranking network to predict the objectness score for each segment proposal. To address the third problem, we take a transformation-based approach to improve the quality of a given segment candidate pool based on context information. We propose an efficient deep network that learns affine transformations to warp an initial object mask towards nearby object region, based on a novel feature pooling strategy. Finally, we extend our affine warping approach to address the object-mask alignment problem and particularly the problem of refining a set of segment proposals. We design an end-to-end deep spatial transformer network that learns free-form deformations (FFDs) to non-rigidly warp the shape mask towards the ground truth, based on a multi-level dual mask feature pooling strategy. We evaluate all our approaches on several publicly available object recognition datasets and show superior performance

Ophthalmologic Image Registration Based on Shape-Context: Application to Fundus Autofluorescence (FAF) Images

Online access to subscriber only at http://www.actapress.com/Content_Of_Proceeding.aspx?ProceedingID=494International audienceA novel registration algorithm, which was developed in order to facilitate ophthalmologic image processing, is presented in this paper. It has been evaluated on FAF images, which present low Si gnal/Noise Ratio (SNR) and variations in dynamic grayscale range. These characteristics complicate the registration process and cause a failure to area-based registration techniques [1, 2] . Our method is based on shape-context theory [3] . In the first step, images are enhanced by Gaussian model based histog ram modification. Features are extracted in the next step by morphological operators, which are used to detect an approximation of vascular tree from both reference and floating images. Simplified medial axis of vessels is then calculated. From each image, a set of control points called Bifurcation Points (BPs) is extracted from the medial axis through a new fast algorithm. Radial histogram is formed for each BP using the medial axis. The Chi2 distance is measured between two sets of BPs based on radial histogram. Hungarian algorithm is applied to assign the correspondence among BPs from reference and floating images. The algorithmic robustness is evaluated by mutual information criteria between manual registration considered as Ground Truth and automatic one

Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Study of Computational Image Matching Techniques: Improving Our View of Biomedical Image Data

Image matching techniques are proven to be necessary in various fields of science and engineering, with many new methods and applications introduced over the years. In this PhD thesis, several computational image matching methods are introduced and investigated for improving the analysis of various biomedical image data. These improvements include the use of matching techniques for enhancing visualization of cross-sectional imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), denoising of retinal Optical Coherence Tomography (OCT), and high quality 3D reconstruction of surfaces from Scanning Electron Microscope (SEM) images. This work greatly improves the process of data interpretation of image data with far reaching consequences for basic sciences research. The thesis starts with a general notion of the problem of image matching followed by an overview of the topics covered in the thesis. This is followed by introduction and investigation of several applications of image matching/registration in biomdecial image processing: a) registration-based slice interpolation, b) fast mesh-based deformable image registration and c) use of simultaneous rigid registration and Robust Principal Component Analysis (RPCA) for speckle noise reduction of retinal OCT images. Moving towards a different notion of image matching/correspondence, the problem of view synthesis and 3D reconstruction, with a focus on 3D reconstruction of microscopic samples from 2D images captured by SEM, is considered next. Starting from sparse feature-based matching techniques, an extensive analysis is provided for using several well-known feature detector/descriptor techniques, namely ORB, BRIEF, SURF and SIFT, for the problem of multi-view 3D reconstruction. This chapter contains qualitative and quantitative comparisons in order to reveal the shortcomings of the sparse feature-based techniques. This is followed by introduction of a novel framework using sparse-dense matching/correspondence for high quality 3D reconstruction of SEM images. As will be shown, the proposed framework results in better reconstructions when compared with state-of-the-art sparse-feature based techniques. Even though the proposed framework produces satisfactory results, there is room for improvements. These improvements become more necessary when dealing with higher complexity microscopic samples imaged by SEM as well as in cases with large displacements between corresponding points in micrographs. Therefore, based on the proposed framework, a new approach is proposed for high quality 3D reconstruction of microscopic samples. While in case of having simpler microscopic samples the performance of the two proposed techniques are comparable, the new technique results in more truthful reconstruction of highly complex samples. The thesis is concluded with an overview of the thesis and also pointers regarding future directions of the research using both multi-view and photometric techniques for 3D reconstruction of SEM images

Improving the Geotagging Accuracy of Street-level Images

Integrating images taken at street-level with satellite imagery is becoming increasingly valuable in the decision-making processes not only for individuals, but also in business and governmental sectors. To perform this integration, images taken at street-level need to be accurately georeferenced. This georeference information can be derived from a global positioning system (GPS). However, GPS data is prone to errors up to 15 meters, and needs to be corrected for the purpose of geo-referencing. In this thesis, an automatic method is proposed for correcting the georeference information obtained from the GPS data, based on image registration techniques. The proposed method uses an optimization technique to find local optimal solutions by matching high-level features and their relative locations. A global optimization method is then employed over all of the local solutions by applying a geometric constraint. The main contribution of this thesis is introducing a new direction for correcting the GPS data which is more economical and more consistent compared to existing manual method. Other than high cost (labor and management), the main concern with manual correction is the low degree of consistency between different human operators. Our proposed automatic software-based method is a solution for these drawbacks. Other contributions can be listed as (1) modified Chamfer matching (CM) cost function which improves the accuracy of standard CM for images with various misleading/disturbing edges; (2) Monte-Carlo-inspired statistical analysis which made it possible to quantify the overall performance of the proposed algorithm; (3) Novel similarity measure for applying normalized cross correlation (NCC) technique on multi-level thresholded images, which is used to compare multi-modal images more accurately as compared to standard application of NCC on raw images. (4) Casting the problem of selecting an optimal global solution among set of local minima into a problem of finding an optimal path in a graph using Dijkstra\u27s algorithm. We used our algorithm for correcting the georeference information of 20 chains containing more than 7000 fisheye images and our experimental results show that the proposed algorithm can achieve an average error of 2 meters, which is acceptable for most of applications