Systematic Parameterization, Storage, and Representation of Volumetric DICOM Data

Tomographic medical imaging systems produce hundreds to thousands of slices, enabling three-dimensional (3D) analysis. Radiologists process these images through various tools and techniques in order to generate 3D renderings for various applications, such as surgical planning, medical education, and volumetric measurements. To save and store these visualizations, current systems use snapshots or video exporting, which prevents further optimizations and requires the storage of significant additional data. The Grayscale Softcopy Presentation State extension of the Digital Imaging and Communications in Medicine (DICOM) standard resolves this issue for two-dimensional (2D) data by introducing an extensive set of parameters, namely 2D Presentation States (2DPR), that describe how an image should be displayed. 2DPR allows storing these parameters instead of storing parameter applied images, which cause unnecessary duplication of the image data. Since there is currently no corresponding extension for 3D data, in this study, a DICOM-compliant object called 3D presentation states (3DPR) is proposed for the parameterization and storage of 3D medical volumes. To accomplish this, the 3D medical visualization process is divided into four tasks, namely pre-processing, segmentation, post-processing, and rendering. The important parameters of each task are determined. Special focus is given to the compression of segmented data, parameterization of the rendering process, and DICOM-compliant implementation of the 3DPR object. The use of 3DPR was tested in a radiology department on three clinical cases, which require multiple segmentations and visualizations during the workflow of radiologists. The results show that 3DPR can effectively simplify the workload of physicians by directly regenerating 3D renderings without repeating intermediate tasks, increase efficiency by preserving all user interactions, and provide efficient storage as well as transfer of visualized data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40846-015-0097-5) contains supplementary material, which is available to authorized users

Near-Lossless Bitonal Image Compression System

The main purpose of this thesis is to develop an efficient near-lossless bitonal compression algorithm and to implement that algorithm on a hardware platform. The current methods for compression of bitonal images include the JBIG and JBIG2 algorithms, however both JBIG and JBIG2 have their disadvantages. Both of these algorithms are covered by patents filed by IBM, making them costly to implement commercially. Also, JBIG only provides means for lossless compression while JBIG2 provides lossy methods only for document-type images. For these reasons a new method for introducing loss and controlling this loss to sustain quality is developed. The lossless bitonal image compression algorithm used for this thesis is called Block Arithmetic Coder for Image Compression (BACIC), which can efficiently compress bitonal images. In this thesis, loss is introduced for cases where better compression efficiency is needed. However, introducing loss in bitonal images is especially difficult, because pixels undergo such a drastic change, either from white to black or black to white. Such pixel flipping introduces salt and pepper noise, which can be very distracting when viewing an image. Two methods are used in combination to control the visual distortion introduced into the image. The first is to keep track of the error created by the flipping of pixels, and using this error to decide whether flipping another pixel will cause the visual distortion to exceed a predefined threshold. The second method is region of interest consideration. In this method, lower loss or no loss is introduced into the important parts of an image, and higher loss is introduced into the less important parts. This allows for a good quality image while increasing the compression efficiency. Also, the ability of BACIC to compress grayscale images is studied and BACICm, a multiplanar BACIC algorithm, is created. A hardware implementation of the BACIC lossless bitonal image compression algorithm is also designed. The hardware implementation is done using VHDL targeting a Xilinx FPGA, which is very useful, because of its flexibility. The programmed FPGA could be included in a product of the facsimile or printing industry to handle the compression or decompression internal to the unit, giving it an advantage in the marketplace

Selection of bilevel image compression methods for reduction of communication energy in wireless vision sensor networks

ABSTRACT Wireless Visual Sensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which operates on one dimensional data, such as temperature, pressure values etc., WVSN operates on two dimensional data (images) which requires higher processing power and communication bandwidth. Normally, WVSNs are deployed in areas where installation of wired solutions is not feasible. The energy budget in these networks is limited to the batteries, because of the wireless nature of the application. Due to the limited availability of energy, the processing at Visual Sensor Nodes (VSN) and communication from VSN to server should consume as low energy as possible. Transmission of raw images wirelessly consumes a lot of energy and requires higher communication bandwidth. Data compression methods reduce data efficiently and hence will be effective in reducing communication cost in WVSN. In this paper, we have compared the compression efficiency and complexity of six well known bi-level image compression methods. The focus is to determine the compression algorithms which can efficiently compress bi-level images and their computational complexity is suitable for computational platform used in WVSNs. These results can be used as a road map for selection of compression methods for different sets of constraints in WVSN

EFFICIENT IMAGE COMPRESSION AND DECOMPRESSION ALGORITHMS FOR OCR SYSTEMS

This paper presents an efficient new image compression and decompression methods for document images, intended for usage in the pre-processing stage of an OCR system designed for needs of the “Nikola Tesla Museum” in Belgrade. Proposed image compression methods exploit the Run-Length Encoding (RLE) algorithm and an algorithm based on document character contour extraction, while an iterative scanline fill algorithm is used for image decompression. Image compression and decompression methods are compared with JBIG2 and JPEG2000 image compression standards. Segmentation accuracy results for ground-truth documents are obtained in order to evaluate the proposed methods. Results show that the proposed methods outperform JBIG2 compression regarding the time complexity, providing up to 25 times lower processing time at the expense of worse compression ratio results, as well as JPEG2000 image compression standard, providing up to 4-fold improvement in compression ratio. Finally, time complexity results show that the presented methods are sufficiently fast for a real time character segmentation system

Multum in parvo: Toward a generic compression method for binary images.

Data compression is an active field of research as the requirements to efficiently store and retrieve data at minimum time and cost persist to date. Lossless or lossy compression of bi-level data, such as binary images, has an equally crucial factor of importance. In this work, we explore a generic, application-independent method for lossless binary image compression. The first component of the proposed algorithm is a predetermined fixed-size codebook comprising 8 x 8-bit blocks of binary images along with the corresponding codes of shorter lengths. The two variations of the codebook--Huffman codes and Arithmetic codes--have yielded considerable compression ratios for various binary images. In order to attain higher compression, we introduce a second component--the row-column reduction coding--which removes additional redundancy. The proposed method is tested on two major areas involving bi-level data. The first area of application consists of binary images. Empirical results suggest that our algorithm outperforms the standard JBIG2 by at least 5% on average. The second area involves images consisting of a predetermined number of discrete colors, such as digital maps and graphs. By separating such images into binary layers, we employed our algorithm and attained efficient compression down to 0.035 bits per pixel. --P.ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b173649

Sistema baseado em técnicas de compressão para o reconhecimento de dígitos manuscritos

Mestrado em Engenharia Eletrónica e TelecomunicaçõesO reconhecimento de dígitos manuscritos é uma habilidade humana adquirida. Com pouco esforço, um humano pode reconhecer adequadamente em milissegundos uma sequência de dígitos manuscritos. Com o auxílio de um computador, esta tarefa de reconhecimento pode ser facilmente automatizada, melhorando um número significativo de processos. A separação do correio postal, a verificação de cheques bancários e operações que têm como entrada de dados dígitos manuscritos estão incluídas num amplo conjunto de aplicações que podem ser realizadas de forma mais eficaz e automatizada. Nos últimos anos, várias técnicas e métodos foram propostos para automatizar o mecanismo de reconhecimento de dígitos manuscritos. No entanto, para resolver esta desafiante questão de reconhecimento de imagem são utilizadas técnicas complexas e computacionalmente muito exigentes de machine learning, como é o caso do deep learning. Nesta dissertação é introduzida uma nova solução para o problema do reconhecimento de dígitos manuscritos, usando métricas de similaridade entre imagens de dígitos. As métricas de similaridade são calculadas com base na compressão de dados, nomeadamente pelo uso de Modelos de Contexto Finito.The Recognition of Handwritten Digits is a human-acquired ability. With little e ort, a human can properly recognize, in milliseconds, a sequence of handwritten digits. With the help of a computer, the task of handwriting recognition can be easily automated, improving and making a signi cant number of processes faster. The postal mail sorting, bank check veri cation and handwritten digit data entry operations are in a wide group of applications that can be performed in a more e ective and automated way. In the recent past years, a number of techniques and methods have been proposed to automate the handwritten digit recognition mechanism. However, to solve this challenging question of image recognition, there are used complex and computationally demanding machine learning techniques, as it is the case of deep learning. In this dissertation is introduced a novel solution to the problem of handwritten digit recognition, using metrics of similarity between digit images. The metrics are computed based on data compression, namely by the use of Finite Context Models

Codage de cartes de profondeur par deformation de courbes elastiques

In multiple-view video plus depth, depth maps can be represented by means of grayscale images and the corresponding temporal sequence can be thought as a standard grayscale video sequence. However depth maps have different properties from natural images: they present large areas of smooth surfaces separated by sharp edges. Arguably the most important information lies in object contours, as a consequence an interesting approach consists in performing a lossless coding of the contour map, possibly followed by a lossy coding of per-object depth values.In this context, we propose a new technique for the lossless coding of object contours, based on the elastic deformation of curves. A continuous evolution of elastic deformations between two reference contour curves can be modelled, and an elastically deformed version of the reference contours can be sent to the decoder with an extremely small coding cost and used as side information to improve the lossless coding of the actual contour. After the main discontinuities have been captured by the contour description, the depth field inside each region is rather smooth. We proposed and tested two different techniques for the coding of the depth field inside each region. The first technique performs the shape-adaptive wavelet transform followed by the shape-adaptive version of SPIHT. The second technique performs a prediction of the depth field from its subsampled version and the set of coded contours. It is generally recognized that a high quality view rendering at the receiver side is possible only by preserving the contour information, since distortions on edges during the encoding step would cause a sensible degradation on the synthesized view and on the 3D perception. We investigated this claim by conducting a subjective quality assessment test to compare an object-based technique and a hybrid block-based techniques for the coding of depth maps.Dans le format multiple-view video plus depth, les cartes de profondeur peuvent être représentées comme des images en niveaux de gris et la séquence temporelle correspondante peut être considérée comme une séquence vidéo standard en niveaux de gris. Cependant les cartes de profondeur ont des propriétés différentes des images naturelles: ils présentent de grandes surfaces lisses séparées par des arêtes vives. On peut dire que l'information la plus importante réside dans les contours de l'objet, en conséquence une approche intéressante consiste à effectuer un codage sans perte de la carte de contour, éventuellement suivie d'un codage lossy des valeurs de profondeur par-objet.Dans ce contexte, nous proposons une nouvelle technique pour le codage sans perte des contours de l'objet, basée sur la déformation élastique des courbes. Une évolution continue des déformations élastiques peut être modélisée entre deux courbes de référence, et une version du contour déformée élastiquement peut être envoyé au décodeur avec un coût de codage très faible et utilisé comme information latérale pour améliorer le codage sans perte du contour réel. Après que les principales discontinuités ont été capturés par la description du contour, la profondeur à l'intérieur de chaque région est assez lisse. Nous avons proposé et testé deux techniques différentes pour le codage du champ de profondeur à l'intérieur de chaque région. La première technique utilise la version adaptative à la forme de la transformation en ondelette, suivie par la version adaptative à la forme de SPIHT.La seconde technique effectue une prédiction du champ de profondeur à partir de sa version sous-échantillonnée et l'ensemble des contours codés. Il est généralement reconnu qu'un rendu de haute qualité au récepteur pour un nouveau point de vue est possible que avec la préservation de l'information de contour, car des distorsions sur les bords lors de l'étape de codage entraînerait une dégradation évidente sur la vue synthétisée et sur la perception 3D. Nous avons étudié cette affirmation en effectuant un test d'évaluation de la qualité perçue en comparant, pour le codage des cartes de profondeur, une technique basée sur la compression d'objects et une techniques de codage vidéo hybride à blocs

Understanding and advancing PDE-based image compression

This thesis is dedicated to image compression with partial differential equations (PDEs). PDE-based codecs store only a small amount of image points and propagate their information into the unknown image areas during the decompression step. For certain classes of images, PDE-based compression can already outperform the current quasi-standard, JPEG2000. However, the reasons for this success are not yet fully understood, and PDE-based compression is still in a proof-of-concept stage. With a probabilistic justification for anisotropic diffusion, we contribute to a deeper insight into design principles for PDE-based codecs. Moreover, by analysing the interaction between efficient storage methods and image reconstruction with diffusion, we can rank PDEs according to their practical value in compression. Based on these observations, we advance PDE-based compression towards practical viability: First, we present a new hybrid codec that combines PDE- and patch-based interpolation to deal with highly textured images. Furthermore, a new video player demonstrates the real-time capacities of PDE-based image interpolation and a new region of interest coding algorithm represents important image areas with high accuracy. Finally, we propose a new framework for diffusion-based image colourisation that we use to build an efficient codec for colour images. Experiments on real world image databases show that our new method is qualitatively competitive to current state-of-the-art codecs.Diese Dissertation ist der Bildkompression mit partiellen Differentialgleichungen (PDEs, partial differential equations) gewidmet. PDE-Codecs speichern nur einen geringen Anteil aller Bildpunkte und transportieren deren Information in fehlende Bildregionen. In einigen Fällen kann PDE-basierte Kompression den aktuellen Quasi-Standard, JPEG2000, bereits schlagen. Allerdings sind die Gründe für diesen Erfolg noch nicht vollständig erforscht, und PDE-basierte Kompression befindet sich derzeit noch im Anfangsstadium. Wir tragen durch eine probabilistische Rechtfertigung anisotroper Diffusion zu einem tieferen Verständnis PDE-basierten Codec-Designs bei. Eine Analyse der Interaktion zwischen effizienten Speicherverfahren und Bildrekonstruktion erlaubt es uns, PDEs nach ihrem Nutzen für die Kompression zu beurteilen. Anhand dieser Einsichten entwickeln wir PDE-basierte Kompression hinsichtlich ihrer praktischen Nutzbarkeit weiter: Wir stellen einen Hybrid-Codec für hochtexturierte Bilder vor, der umgebungsbasierte Interpolation mit PDEs kombiniert. Ein neuer Video-Dekodierer demonstriert die Echtzeitfähigkeit PDE-basierter Interpolation und eine Region-of-Interest-Methode erlaubt es, wichtige Bildbereiche mit hoher Genauigkeit zu speichern. Schlussendlich stellen wir ein neues diffusionsbasiertes Kolorierungsverfahren vor, welches uns effiziente Kompression von Farbbildern ermöglicht. Experimente auf Realwelt-Bilddatenbanken zeigen die Konkurrenzfähigkeit dieses Verfahrens auf