Enhancing a Neurosurgical Imaging System with a PC-based Video Processing Solution

This work presents a PC-based prototype video processing application developed to be used with a specific neurosurgical imaging device, the OPMI® PenteroTM operating microscope, in the Department of Neurosurgery of Helsinki University Central Hospital at Töölö, Helsinki. The motivation for implementing the software was the lack of some clinically important features in the imaging system provided by the microscope. The imaging system is used as an online diagnostic aid during surgery. The microscope has two internal video cameras; one for regular white light imaging and one for near-infrared fluorescence imaging, used for indocyanine green videoangiography. The footage of the microscope’s current imaging mode is accessed via the composite auxiliary output of the device. The microscope also has an external high resolution white light video camera, accessed via a composite output of a separate video hub. The PC was chosen as the video processing platform for its unparalleled combination of prototyping and high-throughput video processing capabilities. A thorough analysis of the platform and efficient video processing methods was conducted in the thesis and the results were used in the design of the imaging station. The features found feasible during the project were incorporated into a video processing application running on a GNU/Linux distribution Ubuntu. The clinical usefulness of the implemented features was ensured beforehand by consulting the neurosurgeons using the original system. The most significant shortcomings of the original imaging system were mended in this work. The key features of the developed application include: live streaming, simultaneous streaming and recording, and playing back of upto two video streams. The playback mode provides full media player controls, with a frame-by-frame precision rewinding, in an intuitive and responsive interface. A single view and a side-by-side comparison mode are provided for the streams. The former gives more detail, while the latter can be used, for example, for before-after and anatomic-angiographic comparisons.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Zooplankton visualization system: design and real-time lossless image compression

In this thesis, I present a design of a small, self-contained, underwater plankton imaging system. I base the imaging system’s design on an embedded PC architecture based on PC/104-Plus standards to meet the compact size and low power requirements. I developed a simple graphical user interface to run on a real-time operating system to control the imaging system. I also address how a real-time image compression scheme implemented on an FPGA chip speeds up image transfer speeds of the imaging system. Since lossless compression of the image is required in order to retain all image details, I began with an established compression scheme like SPIHT, and latter proposed a new compression scheme that suits the imaging system’s requirements. I provide an estimate of the total amount of resources required and propose suitable FPGA chips to implement the compression scheme. Finally, I present various parallel designs by which the FPGA chip can be integrated into the imaging system

Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu

Application guide for universal source encoding for space

Lossless data compression was studied for many NASA missions. The Rice algorithm was demonstrated to provide better performance than other available techniques on most scientific data. A top-level description of the Rice algorithm is first given, along with some new capabilities implemented in both software and hardware forms. Systems issues important for onboard implementation, including sensor calibration, error propagation, and data packetization, are addressed. The latter part of the guide provides twelve case study examples drawn from a broad spectrum of science instruments

Non-disruptive use of light fields in image and video processing

In the age of computational imaging, cameras capture not only an image but also data. This captured additional data can be best used for photo-realistic renderings facilitating numerous post-processing possibilities such as perspective shift, depth scaling, digital refocus, 3D reconstruction, and much more. In computational photography, the light field imaging technology captures the complete volumetric information of a scene. This technology has the highest potential to accelerate immersive experiences towards close-toreality. It has gained significance in both commercial and research domains. However, due to lack of coding and storage formats and also the incompatibility of the tools to process and enable the data, light fields are not exploited to its full potential. This dissertation approaches the integration of light field data to image and video processing. Towards this goal, the representation of light fields using advanced file formats designed for 2D image assemblies to facilitate asset re-usability and interoperability between applications and devices is addressed. The novel 5D light field acquisition and the on-going research on coding frameworks are presented. Multiple techniques for optimised sequencing of light field data are also proposed. As light fields contain complete 3D information of a scene, large amounts of data is captured and is highly redundant in nature. Hence, by pre-processing the data using the proposed approaches, excellent coding performance can be achieved.Im Zeitalter der computergestützten Bildgebung erfassen Kameras nicht mehr nur ein Bild, sondern vielmehr auch Daten. Diese erfassten Zusatzdaten lassen sich optimal für fotorealistische Renderings nutzen und erlauben zahlreiche Nachbearbeitungsmöglichkeiten, wie Perspektivwechsel, Tiefenskalierung, digitale Nachfokussierung, 3D-Rekonstruktion und vieles mehr. In der computergestützten Fotografie erfasst die Lichtfeld-Abbildungstechnologie die vollständige volumetrische Information einer Szene. Diese Technologie bietet dabei das größte Potenzial, immersive Erlebnisse zu mehr Realitätsnähe zu beschleunigen. Deshalb gewinnt sie sowohl im kommerziellen Sektor als auch im Forschungsbereich zunehmend an Bedeutung. Aufgrund fehlender Kompressions- und Speicherformate sowie der Inkompatibilität derWerkzeuge zur Verarbeitung und Freigabe der Daten, wird das Potenzial der Lichtfelder nicht voll ausgeschöpft. Diese Dissertation ermöglicht die Integration von Lichtfelddaten in die Bild- und Videoverarbeitung. Hierzu wird die Darstellung von Lichtfeldern mit Hilfe von fortschrittlichen für 2D-Bilder entwickelten Dateiformaten erarbeitet, um die Wiederverwendbarkeit von Assets- Dateien und die Kompatibilität zwischen Anwendungen und Geräten zu erleichtern. Die neuartige 5D-Lichtfeldaufnahme und die aktuelle Forschung an Kompressions-Rahmenbedingungen werden vorgestellt. Es werden zudem verschiedene Techniken für eine optimierte Sequenzierung von Lichtfelddaten vorgeschlagen. Da Lichtfelder die vollständige 3D-Information einer Szene beinhalten, wird eine große Menge an Daten, die in hohem Maße redundant sind, erfasst. Die hier vorgeschlagenen Ansätze zur Datenvorverarbeitung erreichen dabei eine ausgezeichnete Komprimierleistung

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

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

Sparse representation based hyperspectral image compression and classification

Abstract This thesis presents a research work on applying sparse representation to lossy hyperspectral image compression and hyperspectral image classification. The proposed lossy hyperspectral image compression framework introduces two types of dictionaries distinguished by the terms sparse representation spectral dictionary (SRSD) and multi-scale spectral dictionary (MSSD), respectively. The former is learnt in the spectral domain to exploit the spectral correlations, and the latter in wavelet multi-scale spectral domain to exploit both spatial and spectral correlations in hyperspectral images. To alleviate the computational demand of dictionary learning, either a base dictionary trained offline or an update of the base dictionary is employed in the compression framework. The proposed compression method is evaluated in terms of different objective metrics, and compared to selected state-of-the-art hyperspectral image compression schemes, including JPEG 2000. The numerical results demonstrate the effectiveness and competitiveness of both SRSD and MSSD approaches. For the proposed hyperspectral image classification method, we utilize the sparse coefficients for training support vector machine (SVM) and k-nearest neighbour (kNN) classifiers. In particular, the discriminative character of the sparse coefficients is enhanced by incorporating contextual information using local mean filters. The classification performance is evaluated and compared to a number of similar or representative methods. The results show that our approach could outperform other approaches based on SVM or sparse representation. This thesis makes the following contributions. It provides a relatively thorough investigation of applying sparse representation to lossy hyperspectral image compression. Specifically, it reveals the effectiveness of sparse representation for the exploitation of spectral correlations in hyperspectral images. In addition, we have shown that the discriminative character of sparse coefficients can lead to superior performance in hyperspectral image classification.EM201

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Utilização da Norma JPEG2000 para codificar proteger e comercializar Produtos de Observação Terrestre

Applications like, change detection, global monitoring, disaster detection and management have emerging requirements that need the availability of large amounts of data. This data is currently being capture by a multiplicity of instruments and EO (Earth Observation) sensors originating large volumes of data that needs to be stored, processed and accessed in order to be useful – as an example, ENVISAT accumulates, in a yearly basis, several hundred terabytes of data. This need to recover, store, process and access brings some interesting challenges, like storage space, processing power, bandwidth and security, just to mention a few. These challenges are still very important on today’s technological world. If we take a look for example at the number of subscribers of ISP (Internet Service Providers) broadband services on the developed world today, one can notice that broadband services are still far from being common and dominant. On the underdeveloped countries the picture is even dimmer, not only from a bandwidth point of view but also in all other aspects regarding information and communication technologies (ICTs). All this challenges need to be taken into account if a service is to reach the broadest audience possible. Obviously protection and securing of services and contents is an extra asset that helps on the preservation of possible business values, especially if we consider such a costly business as the space industry. This thesis presents and describes a system which allows, not only the encoding and decoding of several EO products into a JPEG2000 format, but also supports some of the security requirements identified previously that allows ESA (European Space Agency) and related EO services to define and apply efficient EO data access security policies and even to exploit new ways to commerce EO products over the Internet.Aplicações como, detecção de mudanças no terreno, monitorização planetária, detecção e gestão de desastres, têm necessidades prementes que necessitam de vastas quantidades de dados. Estes dados estão presentemente a ser capturados por uma multiplicidade de instrumentos e sensores de observação terrestre, que originam uma enormidade de dados que necessitam de ser armazenados processados e acedidos de forma a se tornarem úteis – por exemplo, a ENVISAT acumula anualmente varias centenas de terabytes de dados. Esta necessidade de recuperar, armazenar, processar e aceder introduz alguns desafios interessantes como o espaço de armazenamento, poder de processamento, largura de banda e segurança dos dados só para mencionar alguns. Estes desafios são muito importantes no mundo tecnológico de hoje. Se olharmos, por exemplo, ao número actual de subscritores de ISP (Internet Service Providers) de banda larga nos países desenvolvidos podemos ficar surpreendidos com o facto do número de subscritores desses serviços ainda não ser uma maioria da população ou dos agregados familiares. Nos países subdesenvolvidos o quadro é ainda mais negro não só do ponto de vista da largura de banda mas também de todos os outros aspectos relacionados com Tecnologias da Informação e Comunicação (TICs). Todos estes aspectos devem ser levados em consideração se se pretende que um serviço se torne o mais abrangente possível em termos de audiências. Obviamente a protecção e segurança dos conteúdos é um factor extra que ajuda a preservar possíveis valores de negócio, especialmente considerando industrias tão onerosas como a Industria Espacial. Esta tese apresenta e descreve um sistema que permite, não só a codificação e descodificação de diversos produtos de observação terrestre para formato JPEG2000 mas também o suporte de alguns requisitos de segurança identificados previamente que permitem, á Agência Espacial Europeia e a outros serviços relacionados com observação terrestre, a aplicação de politicas eficientes de acesso seguro a produtos de observação terrestre, permitindo até o aparecimento de novas forma de comercialização de produtos de observação terrestre através da Internet