Efficient Encoding of Wireless Capsule Endoscopy Images Using Direct Compression of Colour Filter Array Images

Since its invention in 2001, wireless capsule endoscopy (WCE) has played an important role in the endoscopic examination of the gastrointestinal tract. During this period, WCE has undergone tremendous advances in technology, making it the first-line modality for diseases from bleeding to cancer in the small-bowel. Current research efforts are focused on evolving WCE to include functionality such as drug delivery, biopsy, and active locomotion. For the integration of these functionalities into WCE, two critical prerequisites are the image quality enhancement and the power consumption reduction. An efficient image compression solution is required to retain the highest image quality while reducing the transmission power. The issue is more challenging due to the fact that image sensors in WCE capture images in Bayer Colour filter array (CFA) format. Therefore, standard compression engines provide inferior compression performance. The focus of this thesis is to design an optimized image compression pipeline to encode the capsule endoscopic (CE) image efficiently in CFA format. To this end, this thesis proposes two image compression schemes. First, a lossless image compression algorithm is proposed consisting of an optimum reversible colour transformation, a low complexity prediction model, a corner clipping mechanism and a single context adaptive Golomb-Rice entropy encoder. The derivation of colour transformation that provides the best performance for a given prediction model is considered as an optimization problem. The low complexity prediction model works in raster order fashion and requires no buffer memory. The application of colour transformation yields lower inter-colour correlation and allows the efficient independent encoding of the colour components. The second compression scheme in this thesis is a lossy compression algorithm with a integer discrete cosine transformation at its core. Using the statistics obtained from a large dataset of CE image, an optimum colour transformation is derived using the principal component analysis (PCA). The transformed coefficients are quantized using optimized quantization table, which was designed with a focus to discard medically irrelevant information. A fast demosaicking algorithm is developed to reconstruct the colour image from the lossy CFA image in the decoder. Extensive experiments and comparisons with state-of-the-art lossless image compression methods establish the superiority of the proposed compression methods as simple and efficient image compression algorithm. The lossless algorithm can transmit the image in a lossless manner within the available bandwidth. On the other hand, performance evaluation of lossy compression algorithm indicates that it can deliver high quality images at low transmission power and low computation costs

Stereoscopic high dynamic range imaging

Two modern technologies show promise to dramatically increase immersion in virtual environments. Stereoscopic imaging captures two images representing the views of both eyes and allows for better depth perception. High dynamic range (HDR) imaging accurately represents real world lighting as opposed to traditional low dynamic range (LDR) imaging. HDR provides a better contrast and more natural looking scenes. The combination of the two technologies in order to gain advantages of both has been, until now, mostly unexplored due to the current limitations in the imaging pipeline. This thesis reviews both fields, proposes stereoscopic high dynamic range (SHDR) imaging pipeline outlining the challenges that need to be resolved to enable SHDR and focuses on capture and compression aspects of that pipeline. The problems of capturing SHDR images that would potentially require two HDR cameras and introduce ghosting, are mitigated by capturing an HDR and LDR pair and using it to generate SHDR images. A detailed user study compared four different methods of generating SHDR images. Results demonstrated that one of the methods may produce images perceptually indistinguishable from the ground truth. Insights obtained while developing static image operators guided the design of SHDR video techniques. Three methods for generating SHDR video from an HDR-LDR video pair are proposed and compared to the ground truth SHDR videos. Results showed little overall error and identified a method with the least error. Once captured, SHDR content needs to be efficiently compressed. Five SHDR compression methods that are backward compatible are presented. The proposed methods can encode SHDR content to little more than that of a traditional single LDR image (18% larger for one method) and the backward compatibility property encourages early adoption of the format. The work presented in this thesis has introduced and advanced capture and compression methods for the adoption of SHDR imaging. In general, this research paves the way for a novel field of SHDR imaging which should lead to improved and more realistic representation of captured scenes

Guidelines Digital Pathology for Diagnosis on (and Reports of) Digital Images Version 1.0 Bundesverband deutscher Pathologen e.V. (Federal Association of German Pathologist)

Digitalization is entering the medical fields with increasing velocity and impact on diagnostic and therapeutic actions. In addition, it matures to a mandatory tool of quality assurance, reliable inter-disciplinary communication, and promotion of research. The Professional Association of German Pathologists wants to support their members in their thoughts and potential implementation of virtual microscopy and related issues. It founded a committee of digital pathology. Colleagues experienced in routine surgical pathology, information technology and practice have been asked to investigate prerequisites, actual technology stages and financial considerations, and to formulate their recommendations and guidelines. Herein, the official guidelines of the Professional Association of German Pathologists are presented. The guidelines focus on practical issues, Pathologists as well as IT experts or interested researchers are invited to make use of these guidelines. Our readers are also invited to inquire specific tasks or discuss their ideas and experiences. They might either contact the committee directly, or discuss specific points of view by writing a letter to the editor, or by submission of, and to formulate a corresponding interactive publication

Efficient interaction with large medical imaging databases

Everyday, a wide quantity of hospitals and medical centers around the world are producing large amounts of imaging content to support clinical decisions, medical research, and education. With the current trend towards Evidence-based medicine, there is an increasing need of strategies that allow pathologists to properly interact with the valuable information such imaging repositories host and extract relevant content for supporting decision making. Unfortunately, current systems are very limited at providing access to content and extracting information from it because of different semantic and computational challenges. This thesis presents a whole pipeline, comprising 3 building blocks, that aims to to improve the way pathologists and systems interact. The first building block consists in an adaptable strategy oriented to ease the access and visualization of histopathology imaging content. The second block explores the extraction of relevant information from such imaging content by exploiting low- and mid-level information obtained from from morphology and architecture of cell nuclei. The third block aims to integrate high-level information from the expert in the process of identifying relevant information in the imaging content. This final block not only attempts to deal with the semantic gap but also to present an alternative to manual annotation, a time consuming and prone-to-error task. Different experiments were carried out and demonstrated that the introduced pipeline not only allows pathologist to navigate and visualize images but also to extract diagnostic and prognostic information that potentially could support clinical decisions.Resumen: Diariamente, gran cantidad de hospitales y centros médicos de todo el mundo producen grandes cantidades de imágenes diagnósticas para respaldar decisiones clínicas y apoyar labores de investigación y educación. Con la tendencia actual hacia la medicina basada en evidencia, existe una creciente necesidad de estrategias que permitan a los médicos patólogos interactuar adecuadamente con la información que albergan dichos repositorios de imágenes y extraer contenido relevante que pueda ser empleado para respaldar la toma de decisiones. Desafortunadamente, los sistemas actuales son muy limitados en cuanto al acceso y extracción de contenido de las imágenes debido a diferentes desafíos semánticos y computacionales. Esta tesis presenta un marco de trabajo completo para patología, el cual se compone de 3 bloques y tiene como objetivo mejorar la forma en que interactúan los patólogos y los sistemas. El primer bloque de construcción consiste en una estrategia adaptable orientada a facilitar el acceso y la visualización del contenido de imágenes histopatológicas. El segundo bloque explora la extracción de información relevante de las imágenes mediante la explotación de información de características visuales y estructurales de la morfología y la arquitectura de los núcleos celulares. El tercer bloque apunta a integrar información de alto nivel del experto en el proceso de identificación de información relevante en las imágenes. Este bloque final no solo intenta lidiar con la brecha semántica, sino que también presenta una alternativa a la anotación manual, una tarea que demanda mucho tiempo y es propensa a errores. Se llevaron a cabo diferentes experimentos que demostraron que el marco de trabajo presentado no solo permite que el patólogo navegue y visualice imágenes, sino que también extraiga información de diagnóstico y pronóstico que potencialmente podría respaldar decisiones clínicas.Doctorad

Data Compression in Ultrasound Computed Tomography

The large amount of data in the Karlsruhe 3D Ultrasound Computed Tomography (USCT) has to be reduced. For compression of ultrasound signals, cascading bit-wise run length method and adjacent A-scans or samples based method as new lossless methods were developed. Lossy compression methods are evaluated with an image quality based scheme using the newly designed optical flow based and committee model based estimators. Finally, an optimal method with a feasible compression ratio was suggested

The Sixth Annual Workshop on Space Operations Applications and Research (SOAR 1992)

This document contains papers presented at the Space Operations, Applications, and Research Symposium (SOAR) hosted by the U.S. Air Force (USAF) on 4-6 Aug. 1992 and held at the JSC Gilruth Recreation Center. The symposium was cosponsored by the Air Force Material Command and by NASA/JSC. Key technical areas covered during the symposium were robotic and telepresence, automation and intelligent systems, human factors, life sciences, and space maintenance and servicing. The SOAR differed from most other conferences in that it was concerned with Government-sponsored research and development relevant to aerospace operations. The symposium's proceedings include papers covering various disciplines presented by experts from NASA, the USAF, universities, and industry

Interpretation of Mutations, Expression, Copy Number in Somatic Breast Cancer: Implications for Metastasis and Chemotherapy

Breast cancer (BC) patient management has been transformed over the last two decades due to the development and application of genome-wide technologies. The vast amounts of data generated by these assays, however, create new challenges for accurate and comprehensive analysis and interpretation. This thesis describes novel methods for fluorescence in-situ hybridization (FISH), array comparative genomic hybridization (aCGH), and next generation DNA- and RNA-sequencing, to improve upon current approaches used for these technologies. An ab initio algorithm was implemented to identify genomic intervals of single copy and highly divergent repetitive sequences that were applied to FISH and aCGH probe design. FISH probes with higher resolution than commercially available reagents were developed and validated on metaphase chromosomes. An aCGH microarray was developed that had improved reproducibility compared to the standard Agilent 44K array, which was achieved by placing oligonucleotide probes distant from conserved repetitive sequences. Splicing mutations are currently underrepresented in genome-wide sequencing analyses, and there are limited methods to validate genome-wide mutation predictions. This thesis describes Veridical, a program developed to statistically validate aberrant splicing caused by a predicted mutation. Splicing mutation analysis was performed on a large subset of BC patients previously analyzed by the Cancer Genome Atlas. This analysis revealed an elevated number of splicing mutations in genes involved in NCAM pathways in basal-like and HER2-enriched lymph node positive tumours. Genome-wide technologies were leveraged further to develop chemosensitivity models that predict BC response to paclitaxel and gemcitabine. A type of machine learning, called support vector machines (SVM), was used to create predictive models from small sets of biologically-relevant genes to drug disposition or resistance. SVM models generated were able to predict sensitivity in two groups of independent patient data. High variability between individuals requires more accurate and higher resolution genomic data. However the data themselves are insufficient; also needed are more insightful analytical methods to fully exploit these data. This dissertation presents both improvements in data quality and accuracy as well as analytical procedures, with the aim of detecting and interpreting critical genomic abnormalities that are hallmarks of BC subtypes, metastasis and therapy response