Measures used to evaluate the performance of AD algorithms, by using images with available ground truths. Most of them are based on the coincidences (true positives and true negatives) and not coincidences (false positives and false negatives) between regions detected/classified by algorithms under assessment and the corresponding regions in the ground truth.
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.