Deep learning based disease detection and segmentation algorithms promise to improve many clinical processes. However, such require vast amounts of annotated training data, which are typically not available in the medical context due data privacy, legal obstructions, non-uniform acquisition protocols. Synthetic databases with pathologies could provide required data. We demonstrate example ische...

Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applica...

Biomedical image processing has experienced dramatic expansion, and has been an interdisciplinary research field attracting expertise from applied mathematics, computer sciences, engineering, statistics, physics, biology and medicine. Computer-aided diagnostic processing has already become an important part of clinical routine. Accompanied by a rush of new development of high technology and use...

The paper deals with the problem of converting medical images from one format to another. In solving it the structure of the most commonly used medical image formats are studied and analysed. A mechanism for medical image file conversion and a Medical Internal Image Format (MIIF) is proposed as a solution. A medical image conversion system (MICS), designed and implemented to perform conversions...

By image analysis we mean the operation of extracting a relativeiy small amount of quantitative information from a two-dimensional image. Typically, a two-dimensional image, in digital form, consists of an array of 512 x 512 eight-bit digital words. The quantitative information to be extracted consists of a few thousand digital words. These words may describe, for example, the contours of cell ...