7 research outputs found
CompaCT: Fractal-Based Heuristic Pixel Segmentation for Lossless Compression of High-Color DICOM Medical Images
Medical image compression is a widely studied field of data processing due to
its prevalence in modern digital databases. This domain requires a high color
depth of 12 bits per pixel component for accurate analysis by physicians,
primarily in the DICOM format. Standard raster-based compression of images via
filtering is well-known; however, it remains suboptimal in the medical domain
due to non-specialized implementations. This study proposes a lossless medical
image compression algorithm, CompaCT, that aims to target spatial features and
patterns of pixel concentration for dynamically enhanced data processing. The
algorithm employs fractal pixel traversal coupled with a novel approach of
segmentation and meshing between pixel blocks for preprocessing. Furthermore,
delta and entropy coding are applied to this concept for a complete compression
pipeline. The proposal demonstrates that the data compression achieved via
fractal segmentation preprocessing yields enhanced image compression results
while remaining lossless in its reconstruction accuracy. CompaCT is evaluated
in its compression ratios on 3954 high-color CT scans against the efficiency of
industry-standard compression techniques (i.e., JPEG2000, RLE, ZIP, PNG). Its
reconstruction performance is assessed with error metrics to verify lossless
image recovery after decompression. The results demonstrate that CompaCT can
compress and losslessly reconstruct medical images, being 37% more
space-efficient than industry-standard compression systems.Comment: (8/24/2023) v1a: 16 pages, 9 figures, Word PD
The 1995 Science Information Management and Data Compression Workshop
This document is the proceedings from the 'Science Information Management and Data Compression Workshop,' which was held on October 26-27, 1995, at the NASA Goddard Space Flight Center, Greenbelt, Maryland. The Workshop explored promising computational approaches for handling the collection, ingestion, archival, and retrieval of large quantities of data in future Earth and space science missions. It consisted of fourteen presentations covering a range of information management and data compression approaches that are being or have been integrated into actual or prototypical Earth or space science data information systems, or that hold promise for such an application. The Workshop was organized by James C. Tilton and Robert F. Cromp of the NASA Goddard Space Flight Center
Sparse image approximation with application to flexible image coding
Natural images are often modeled through piecewise-smooth regions. Region edges, which correspond to the contours of the objects, become, in this model, the main information of the signal. Contours have the property of being smooth functions along the direction of the edge, and irregularities on the perpendicular direction. Modeling edges with the minimum possible number of terms is of key importance for numerous applications, such as image coding, segmentation or denoising. Standard separable basis fail to provide sparse enough representation of contours, due to the fact that this kind of basis do not see the regularity of edges. In order to be able to detect this regularity, a new method based on (possibly redundant) sets of basis functions able to capture the geometry of images is needed. This thesis presents, in a first stage, a study about the features that basis functions should have in order to provide sparse representations of a piecewise-smooth image. This study emphasizes the need for edge-adapted basis functions, capable to accurately capture local orientation and anisotropic scaling of image structures. The need of different anisotropy degrees and orientations in the basis function set leads to the use of redundant dictionaries. However, redundant dictionaries have the inconvenience of giving no unique sparse image decompositions, and from all the possible decompositions of a signal in a redundant dictionary, just the sparsest is needed. There are several algorithms that allow to find sparse decompositions over redundant dictionaries, but most of these algorithms do not always guarantee that the optimal approximation has been recovered. To cope with this problem, a mathematical study about the properties of sparse approximations is performed. From this, a test to check whether a given sparse approximation is the sparsest is provided. The second part of this thesis presents a novel image approximation scheme, based on the use of a redundant dictionary. This scheme allows to have a good approximation of an image with a number of terms much smaller than the dimension of the signal. This novel approximation scheme is based on a dictionary formed by a combination of anisotropically refined and rotated wavelet-like mother functions and Gaussians. An efficient Full Search Matching Pursuit algorithm to perform the image decomposition in such a dictionary is designed. Finally, a geometric image coding scheme based on the image approximated over the anisotropic and rotated dictionary of basis functions is designed. The coding performances of this dictionary are studied. Coefficient quantization appears to be of crucial importance in the design of a Matching Pursuit based coding scheme. Thus, a quantization scheme for the MP coefficients has been designed, based on the theoretical energy upper bound of the MP algorithm and the empirical observations of the coefficient distribution and evolution. Thanks to this quantization, our image coder provides low to medium bit-rate image approximations, while it allows for on the fly resolution switching and several other affine image transformations to be performed directly in the transformed domain
Compression, pose tracking, and halftoning
In this thesis, we discuss image compression, pose tracking, and halftoning. Although these areas seem to be unrelated at first glance, they can be connected through video coding as application scenario. Our first contribution is an image compression algorithm based on a rectangular subdivision scheme which stores only a small subsets of the image points. From these points, the remained of the image is reconstructed using partial differential equations. Afterwards, we present a pose tracking algorithm that is able to follow the 3-D position and orientation of multiple objects simultaneously. The algorithm can deal with noisy sequences, and naturally handles both occlusions between different objects, as well as occlusions occurring in kinematic chains. Our third contribution is a halftoning algorithm based on electrostatic principles, which can easily be adjusted to different settings through a number of extensions. Examples include modifications to handle varying dot sizes or hatching. In the final part of the thesis, we show how to combine our image compression, pose tracking, and halftoning algorithms to novel video compression codecs. In each of these four topics, our algorithms yield excellent results that outperform those of other state-of-the-art algorithms.In dieser Arbeit werden die auf den ersten Blick vollkommen voneinander unabhängig erscheinenden Bereiche Bildkompression, 3D-Posenschätzung und Halbtonverfahren behandelt und im Bereich der Videokompression sinnvoll zusammengeführt. Unser erster Beitrag ist ein Bildkompressionsalgorithmus, der auf einem rechteckigen Unterteilungsschema basiert. Dieser Algorithmus speichert nur eine kleine Teilmenge der im Bild vorhandenen Punkte, während die restlichen Punkte mittels partieller Differentialgleichungen rekonstruiert werden. Danach stellen wir ein Posenschätzverfahren vor, welches die 3D-Position und Ausrichtung von mehreren Objekten anhand von Bilddaten gleichzeitig verfolgen kann. Unser Verfahren funktioniert bei verrauschten Videos und im Falle von Objektüberlagerungen. Auch Verdeckungen innerhalb einer kinematischen Kette werden natürlich behandelt. Unser dritter Beitrag ist ein Halbtonverfahren, das auf elektrostatischen Prinzipien beruht. Durch eine Reihe von Erweiterungen kann dieses Verfahren flexibel an verschiedene Szenarien angepasst werden. So ist es beispielsweise möglich, verschiedene Punktgrößen zu verwenden oder Schraffuren zu erzeugen. Der letzte Teil der Arbeit zeigt, wie man unseren Bildkompressionsalgorithmus, unser Posenschätzverfahren und unser Halbtonverfahren zu neuen Videokompressionsalgorithmen kombinieren kann. Die für jeden der vier Themenbereiche entwickelten Verfahren erzielen hervorragende Resultate, welche die Ergebnisse anderer moderner Verfahren übertreffen
Image Registration Workshop Proceedings
Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research
Recommended from our members
Computational Methods in Multi-Messenger Astrophysics using Gravitational Waves and High Energy Neutrinos
This dissertation seeks to describe advancements made in computational methods for multi-messenger astrophysics (MMA) using gravitational waves GW and neutrinos during Advanced LIGO (aLIGO)’s first through third observing runs (O1-O3) and, looking forward, to describe novel computational techniques suited to the challenges of both the burgeoning MMA field and high-performance computing as a whole.
The first two chapters provide an overview of MMA as it pertains to gravitational wave/high energy neutrino (GWHEN) searches, including a summary of expected astrophysical sources as well as GW, neutrino, and gamma-ray detectors used in their detection. These are followed in the third chapter by an in-depth discussion of LIGO’s timing system, particularly the diagnostic subsystem, describing both its role in MMA searches and the author’s contributions to the system itself.
The fourth chapter provides a detailed description of the Low-Latency Algorithm for Multi-messenger Astrophysics (LLAMA), the GWHEN pipeline developed by the author and used in O2 and O3. Relevant past multi-messenger searches are described first, followed by the O2 and O3 analysis methods, the pipeline’s performance, scientific results, and finally, an in-depth account of the library’s structure and functionality. In particular, the author’s high-performance multi-order coordinates (MOC) HEALPix image analysis library, HPMOC, is described. HPMOC increases performance of HEALPix image manipulations by several orders of magnitude vs. naive single-resolution approaches while presenting a simple high-level interface and should prove useful for diverse future MMA searches. The performance improvements it provides for LLAMA are also covered.
The final chapter of this dissertation builds on the approaches taken in developing HPMOC, presenting several novel methods for efficiently storing and analyzing large data sets, with applications to MMA and other data-intensive fields. A family of depth-first multi-resolution ordering of HEALPix images — DEPTH9, DEPTH19, and DEPTH40 — is defined, along with algorithms and use cases where it can improve on current approaches, including high-speed streaming calculations suitable for serverless compute or FPGAs.
For performance-constrained analyses on HEALPix data (e.g. image analysis in multi-messenger search pipelines) using SIMD processors, breadth-first data structures can provide short-circuiting calculations in a data-parallel way on compressed data; a simple compression method is described with application to further improving LLAMA performance.
A new storage scheme and associated algorithms for efficiently compressing and contracting tensors of varying sparsity is presented; these demuxed tensors (D-Tensors) have equivalent asymptotic time and space complexity to optimal representations of both dense and sparse matrices, and could be used as a universal drop-in replacement to reduce code complexity and developer effort while improving performance of existing non-optimized numerical code. Finally, the big bucket hash table (B-Table), a novel type of hash table making guarantees on data layout (vs. load factor), is described, along with optimizations it allows for (like hardware acceleration, online rebuilds, and hard realtime applications) that are not possible with existing hash table approaches. These innovations are presented in the hope that some will prove useful for improving future MMA searches and other data-intensive applications