Holographic image archive

A review of intelligent content-based indexing and browsing of medical images

Physicians are beginning to be able to gain access, through the Internet, to the world’s collections of multimedia medical information such as MRI (magnetic resonance imaging) and CT (computer tomography) image archives, videos of surgical operations and medical lectures, textual patient records and media annotations. New techniques and tools are needed to represent, index, store and retrieve digital content efficiently across large collections. In this review, we trace the development of visual information systems for healthcare and medicine from Picture Archiving and Communications Systems (PACS) to the recent advances in content-based image retrieval, whereby images are retrieved based on their visual content similarity - that is, colour, texture, and shape. Medical images, unlike consumer-oriented images, pose additional challenges to content-based image retrieval, in that visual features of normal and pathological images are typically separated by only subtle differences in visual appearance. Intelligent image retrieval and browsing therefore requires a combination of prior knowledge of the medical domain, image content and image annotation analysis. To this end, we also overview the I-Browseproject, conducted jointly by the Clinical School of the University of Cambridge and the City University of Hong Kong, which aims to develop techniques which enable a physician to search over image archives through a combination of semantic and iconic contents.

A review of content-based image retrieval systems in medical applications-clinical benefits and future directions

Content-based visual information retrieval (CBVIR) or content-based image retrieval (CBIR) has been one on the most vivid research areas in the field of computer vision over the last 10 years. The availability of large and steadily growing amounts of visual and multimedia data, and the development of the Internet underline the need to create thematic access methods that offer more than simple text-based queries or requests based on matching exact database fields. Many programs and tools have been developed to formulate and execute queries based on the visual or audio content and to help browsing large multimedia repositories. Still, no general breakthrough has been achieved with respect to large varied databases with documents of differing sorts and with varying characteristics. Answers to many questions with respect to speed, semantic descriptors or objective image interpretations are still unanswered. In the medical field, images, and especially digital images, are produced in ever-increasing quantities and used for diagnostics and therapy. The Radiology Department of the University Hospital of Geneva alone produced more than 12,000 images a day in 2002. The cardiology is currently the second largest producer of digital images, especially with videos of cardiac catheterization ( approximately 1800 exams per year containing almost 2000 images each). The total amount of cardiologic image data produced in the Geneva University Hospital was around 1 TB in 2002. Endoscopic videos can equally produce enormous amounts of data. With digital imaging and communications in medicine (DICOM), a standard for image communication has been set and patient information can be stored with the actual image(s), although still a few problems prevail with respect to the standardization. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into picture archiving and communication systems (PACS) have been created. This article gives an overview of available literature in the field of content-based access to medical image data and on the technologies used in the field. Section 1 gives an introduction into generic content-based image retrieval and the technologies used. Section 2 explains the propositions for the use of image retrieval in medical practice and the various approaches. Example systems and application areas are described. Section 3 describes the techniques used in the implemented systems, their datasets and evaluations. Section 4 identifies possible clinical benefits of image retrieval systems in clinical practice as well as in research and education. New research directions are being defined that can prove to be useful. This article also identifies explanations to some of the outlined problems in the field as it looks like many propositions for systems are made from the medical domain and research prototypes are developed in computer science departments using medical datasets. Still, there are very few systems that seem to be used in clinical practice. It needs to be stated as well that the goal is not, in general, to replace text-based retrieval methods as they exist at the moment but to complement them with visual search tools.

Digitizing life at the level of the cell: high-performance laser-scanning microscopy and image analysis for in toto imaging of development

The field of biological imaging is progressing at an amazing rate. Advances in both laser-scanning microscopy and green fluorescent protein (GFP) technology are combining to make possible imaging-based approaches for studying developmental mechanisms that were previously impossible. Modern confocal and multi-photon microscopes are pushing the envelope of speed, sensitivity, spectral resolution, and depth resolution to allow in vivo imaging of whole, live embryos at cellular resolution over extended periods of time. In toto imaging, in which nearly every cell in an embryo or tissue can be tracked through space and time during development, may become a standard technique for small transparent embryos such as zebrafish and early stage chick and mouse embryos. GFP and its spectral variants can be used to mark a wide range of in vivo biological information for in toto imaging including gene expression patterns, mutant phenotypes, and protein subcellular localization patterns. Combining in toto imaging and GFP transgenic approaches on a large scale may usher in an explosion of in vivo, developmental data as has happened in the past several years with genomic data. There are significant challenges that must be met to reach these goals. This paper will discuss the current state-of-the-art, the challenges, and the prospects of in toto imaging in the areas of imaging, image analysis, and informatics.