Mapping of portions of the image dynamic range to the dynamic range of the display monitor. For instance, a 12 bit CT image should be re-scaled for brain matter analysis with a window/level adjustment around 35 ± 35 Hounsfield Units (i.e. gray-levels between 1000 and 1070). This is the base of the variable-bin-size histogram approach in this work.
Published in Chapter:
Anomaly Detection in Medical Image Analysis
Alberto Taboada-Crispi (Universidad Central de Las Villas, Cuba), Hichem Sahli (Universiteit Brussel, Belgium), Denis Hernandez-Pacheco (Universidad Central de Las Villas, Cuba), and Alexander Falcon-Ruiz (Universidad Central de Las Villas, Cuba)
Copyright: © 2009
|Pages: 21
DOI: 10.4018/978-1-60566-314-2.ch027
Abstract
Various approaches have been taken to detect anomalies, with certain particularities in the medical image scenario, linked to other terms: content-based image retrieval, pattern recognition, classification, segmentation, outlier detection, image mining, as well as computer-assisted diagnosis, and computeraided surgery. This chapter presents, a review of anomaly detection (AD) techniques and assessment methodologies, which have been applied to medical images, emphasizing their peculiarities, limitations and future perspectives. Moreover, a contribution to the field of AD in brain computed tomography images is also given, illustrated and assessed.