Derivation of an observer model adapted to irregular signals based on convolution channels.
Details
State: Public
Version: author
Serval ID
serval:BIB_E6A575B456A0
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Derivation of an observer model adapted to irregular signals based on convolution channels.
Journal
Ieee Transactions On Medical Imaging
ISSN
1558-254X (Electronic)
ISSN-L
0278-0062
Publication state
Published
Issued date
2015
Peer-reviewed
Oui
Volume
34
Number
7
Pages
1428-1435
Language
english
Abstract
Anthropomorphic model observers are mathe- matical algorithms which are applied to images with the ultimate goal of predicting human signal detection and classification accuracy across varieties of backgrounds, image acquisitions and display conditions. A limitation of current channelized model observers is their inability to handle irregularly-shaped signals, which are common in clinical images, without a high number of directional channels. Here, we derive a new linear model observer based on convolution channels which we refer to as the "Filtered Channel observer" (FCO), as an extension of the channelized Hotelling observer (CHO) and the nonprewhitening with an eye filter (NPWE) observer. In analogy to the CHO, this linear model observer can take the form of a single template with an external noise term. To compare with human observers, we tested signals with irregular and asymmetrical shapes spanning the size of lesions down to those of microcalfications in 4-AFC breast tomosynthesis detection tasks, with three different contrasts for each case. Whereas humans uniformly outperformed conventional CHOs, the FCO observer outperformed humans for every signal with only one exception. Additive internal noise in the models allowed us to degrade model performance and match human performance. We could not match all the human performances with a model with a single internal noise component for all signal shape, size and contrast conditions. This suggests that either the internal noise might vary across signals or that the model cannot entirely capture the human detection strategy. However, the FCO model offers an efficient way to apprehend human observer performance for a non-symmetric signal.
Pubmed
Web of science
Open Access
Yes
Create date
01/08/2015 9:54
Last modification date
20/08/2019 17:09