Browse by author
Lookup NU author(s): Dr Anando SenORCiD
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Mathematical model observers that are applicable for clinically realistic tasks are of particular interest for task-based assessments. We propose an efficient search-capable model observer that can operate without explicit background knowledge. In place of existing scanning-observer frameworks that invoke background subtraction, the model generates adaptive binary discriminants from feature data containing implicit background information. Initial validation of the model against human-observer data from a PET localization ROC (LROC) study is presented.
Author(s): Gifford HC, Sen A, Azencott R
Editor(s): King M; Glick S; Mueller K
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 13th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Year of Conference: 2015
Online publication date: 01/06/2015
Acceptance date: 19/02/2015