3D Medical Image Lossless Compressor Using Deep Learning Approaches

The ever-increasing importance of accelerated information processing, communica-tion, and storing are major requirements within the big-data era revolution. With the extensive rise in data availability, handy information acquisition, and growing data rate, a critical challenge emerges in efficient handling. Even with advanced technical hardware developments and multiple Graphics Processing Units (GPUs) availability, this demand is still highly promoted to utilise these technologies effectively. Health-care systems are one of the domains yielding explosive data growth. Especially when considering their modern scanners abilities, which annually produce higher-resolution and more densely sampled medical images, with increasing requirements for massive storage capacity. The bottleneck in data transmission and storage would essentially be handled with an effective compression method. Since medical information is critical and imposes an influential role in diagnosis accuracy, it is strongly encouraged to guarantee exact reconstruction with no loss in quality, which is the main objective of any lossless compression algorithm. Given the revolutionary impact of Deep Learning (DL) methods in solving many tasks while achieving the state of the art results, includ-ing data compression, this opens tremendous opportunities for contributions. While considerable efforts have been made to address lossy performance using learning-based approaches, less attention was paid to address lossless compression. This PhD thesis investigates and proposes novel learning-based approaches for compressing 3D medical images losslessly.Firstly, we formulate the lossless compression task as a supervised sequential prediction problem, whereby a model learns a projection function to predict a target voxel given sequence of samples from its spatially surrounding voxels. Using such 3D local sampling information efficiently exploits spatial similarities and redundancies in a volumetric medical context by utilising such a prediction paradigm. The proposed NN-based data predictor is trained to minimise the differences with the original data values while the residual errors are encoded using arithmetic coding to allow lossless reconstruction.Following this, we explore the effectiveness of Recurrent Neural Networks (RNNs) as a 3D predictor for learning the mapping function from the spatial medical domain (16 bit-depths). We analyse Long Short-Term Memory (LSTM) models’ generalisabil-ity and robustness in capturing the 3D spatial dependencies of a voxel’s neighbourhood while utilising samples taken from various scanning settings. We evaluate our proposed MedZip models in compressing unseen Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) modalities losslessly, compared to other state-of-the-art lossless compression standards.This work investigates input configurations and sampling schemes for a many-to-one sequence prediction model, specifically for compressing 3D medical images (16 bit-depths) losslessly. The main objective is to determine the optimal practice for enabling the proposed LSTM model to achieve a high compression ratio and fast encoding-decoding performance. A solution for a non-deterministic environments problem was also proposed, allowing models to run in parallel form without much compression performance drop. Compared to well-known lossless codecs, experimental evaluations were carried out on datasets acquired by different hospitals, representing different body segments, and have distinct scanning modalities (i.e. CT and MRI).To conclude, we present a novel data-driven sampling scheme utilising weighted gradient scores for training LSTM prediction-based models. The objective is to determine whether some training samples are significantly more informative than others, specifically in medical domains where samples are available on a scale of billions. The effectiveness of models trained on the presented importance sampling scheme was evaluated compared to alternative strategies such as uniform, Gaussian, and sliced-based sampling

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Multi-frame reconstruction using super-resolution, inpainting, segmentation and codecs

In this thesis, different aspects of video and light field reconstruction are considered such as super-resolution, inpainting, segmentation and codecs. For this purpose, each of these strategies are analyzed based on a specific goal and a specific database. Accordingly, databases which are relevant to film industry, sport videos, light fields and hyperspectral videos are used for the sake of improvement. This thesis is constructed around six related manuscripts, in which several approaches are proposed for multi-frame reconstruction. Initially, a novel multi-frame reconstruction strategy is proposed for lightfield super-resolution in which graph-based regularization is applied along with edge preserving filtering for improving the spatio-angular quality of lightfield. Second, a novel video reconstruction is proposed which is built based on compressive sensing (CS), Gaussian mixture models (GMM) and sparse 3D transform-domain block matching. The motivation of the proposed technique is the improvement in visual quality performance of the video frames and decreasing the reconstruction error in comparison with the former video reconstruction methods. In the next approach, student-t mixture models and edge preserving filtering are applied for the purpose of video super-resolution. Student-t mixture model has a heavy tail which makes it robust and suitable as a video frame patch prior and rich in terms of log likelihood for information retrieval. In another approach, a hyperspectral video database is considered, and a Bayesian dictionary learning process is used for hyperspectral video super-resolution. To that end, Beta process is used in Bayesian dictionary learning and a sparse coding is generated regarding the hyperspectral video super-resolution. The spatial super-resolution is followed by a spectral video restoration strategy, and the whole process leveraged two different dictionary learnings, in which the first one is trained for spatial super-resolution and the second one is trained for the spectral restoration. Furthermore, in another approach, a novel framework is proposed for replacing advertisement contents in soccer videos in an automatic way by using deep learning strategies. For this purpose, a UNET architecture is applied (an image segmentation convolutional neural network technique) for content segmentation and detection. Subsequently, after reconstructing the segmented content in the video frames (considering the apparent loss in detection), the unwanted content is replaced by new one using a homography mapping procedure. In addition, in another research work, a novel video compression framework is presented using autoencoder networks that encode and decode videos by using less chroma information than luma information. For this purpose, instead of converting Y'CbCr 4:2:2/4:2:0 videos to and from RGB 4:4:4, the video is kept in Y'CbCr 4:2:2/4:2:0 and merged the luma and chroma channels after the luma is downsampled to match the chroma size. An inverse function is performed for the decoder. The performance of these models is evaluated by using CPSNR, MS-SSIM, and VMAF metrics. The experiments reveal that, as compared to video compression involving conversion to and from RGB 4:4:4, the proposed method increases the video quality by about 5.5% for Y'CbCr 4:2:2 and 8.3% for Y'CbCr 4:2:0 while reducing the amount of computation by nearly 37% for Y'CbCr 4:2:2 and 40% for Y'CbCr 4:2:0. The thread that ties these approaches together is reconstruction of the video and light field frames based on different aspects of problems such as having loss of information, blur in the frames, existing noise after reconstruction, existing unpleasant content, excessive size of information and high computational overhead. In three of the proposed approaches, we have used Plug-and-Play ADMM model for the first time regarding reconstruction of videos and light fields in order to address both information retrieval in the frames and tackling noise/blur at the same time. In two of the proposed models, we applied sparse dictionary learning to reduce the data dimension and demonstrate them as an efficient linear combination of basis frame patches. Two of the proposed approaches are developed in collaboration with industry, in which deep learning frameworks are used to handle large set of features and to learn high-level features from the data

Technology 2002: The Third National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2002 Conference and Exposition, December 1-3, 1992, Baltimore, MD. Volume 2 features 60 papers presented during 30 concurrent sessions

Solutions for large scale, efficient, and secure Internet of Things

The design of a general architecture for the Internet of Things (IoT) is a complex task, due to the heterogeneity of devices, communication technologies, and applications that are part of such systems. Therefore, there are significant opportunities to improve the state of the art, whether to better the performance of the system, or to solve actual issues in current systems. This thesis focuses, in particular, on three aspects of the IoT. First, issues of cyber-physical systems are analysed. In these systems, IoT technologies are widely used to monitor, control, and act on physical entities. One of the most important issue in these scenarios are related to the communication layer, which must be characterized by high reliability, low latency, and high energy efficiency. Some solutions for the channel access scheme of such systems are proposed, each tailored to different specific scenarios. These solutions, which exploit the capabilities of state of the art radio transceivers, prove effective in improving the performance of the considered systems. Positioning services for cyber-physical systems are also investigated, in order to improve the accuracy of such services. Next, the focus moves to network and service optimization for traffic intensive applications, such as video streaming. This type of traffic is common amongst non-constrained devices, like smartphones and augmented/virtual reality headsets, which form an integral part of the IoT ecosystem. The proposed solutions are able to increase the video Quality of Experience while wasting less bandwidth than state of the art strategies. Finally, the security of IoT systems is investigated. While often overlooked, this aspect is fundamental to enable the ubiquitous deployment of IoT. Therefore, security issues of commonly used IoT protocols are presented, together with a proposal for an authentication mechanism based on physical channel features. This authentication strategy proved to be effective as a standalone mechanism or as an additional security layer to improve the security level of legacy systems

Compression et transmission d'images avec énergie minimale application aux capteurs sans fil

Un réseau de capteurs d'images sans fil (RCISF) est un réseau ad hoc formé d'un ensemble de noeuds autonomes dotés chacun d'une petite caméra, communiquant entre eux sans liaison filaire et sans l'utilisation d'une infrastructure établie, ni d'une gestion de réseau centralisée. Leur utilité semble majeure dans plusieurs domaines, notamment en médecine et en environnement. La conception d'une chaîne de compression et de transmission sans fil pour un RCISF pose de véritables défis. L'origine de ces derniers est liée principalement à la limitation des ressources des capteurs (batterie faible , capacité de traitement et mémoire limitées). L'objectif de cette thèse consiste à explorer des stratégies permettant d'améliorer l'efficacité énergétique des RCISF, notamment lors de la compression et de la transmission des images. Inéluctablement, l'application des normes usuelles telles que JPEG ou JPEG2000 est éner- givore, et limite ainsi la longévité des RCISF. Cela nécessite leur adaptation aux contraintes imposées par les RCISF. Pour cela, nous avons analysé en premier lieu, la faisabilité d'adapter JPEG au contexte où les ressources énergétiques sont très limitées. Les travaux menés sur cet aspect nous permettent de proposer trois solutions. La première solution est basée sur la propriété de compactage de l'énergie de la Transformée en Cosinus Discrète (TCD). Cette propriété permet d'éliminer la redondance dans une image sans trop altérer sa qualité, tout en gagnant en énergie. La réduction de l'énergie par l'utilisation des régions d'intérêts représente la deuxième solution explorée dans cette thèse. Finalement, nous avons proposé un schéma basé sur la compression et la transmission progressive, permettant ainsi d'avoir une idée générale sur l'image cible sans envoyer son contenu entier. En outre, pour une transmission non énergivore, nous avons opté pour la solution suivante. N'envoyer fiablement que les basses fréquences et les régions d'intérêt d'une image. Les hautes fréquences et les régions de moindre intérêt sont envoyées""infiablement"", car leur pertes n'altèrent que légèrement la qualité de l'image. Pour cela, des modèles de priorisation ont été comparés puis adaptés à nos besoins. En second lieu, nous avons étudié l'approche par ondelettes (wavelets ). Plus précisément, nous avons analysé plusieurs filtres d'ondelettes et déterminé les ondelettes les plus adéquates pour assurer une faible consommation en énergie, tout en gardant une bonne qualité de l'image reconstruite à la station de base. Pour estimer l'énergie consommée par un capteur durant chaque étape de la 'compression, un modèle mathématique est développé pour chaque transformée (TCD ou ondelette). Ces modèles, qui ne tiennent pas compte de la complexité de l'implémentation, sont basés sur le nombre d'opérations de base exécutées à chaque étape de la compression