Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels
(2015) In IEEE Transactions on Medical Imaging 34(7). p.1428-1435- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7790839
- author
- Diaz, Ivan ; Abbey, Craig K. ; Timberg, Pontus LU ; Eckstein, Miguel P. ; Verdun, Francis R. ; Castella, Cyril and Bochud, Francois O.
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Image quality assessment, model observers, optimization
- in
- IEEE Transactions on Medical Imaging
- volume
- 34
- issue
- 7
- pages
- 1428 - 1435
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- wos:000357614300002
- scopus:84936797151
- pmid:25622313
- ISSN
- 1558-254X
- DOI
- 10.1109/TMI.2015.2395433
- language
- English
- LU publication?
- yes
- id
- 700c0efc-242c-4a3a-8403-0ffc9084286c (old id 7790839)
- date added to LUP
- 2016-04-01 11:04:49
- date last changed
- 2022-04-28 06:58:42
@article{700c0efc-242c-4a3a-8403-0ffc9084286c,
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.}},
author = {{Diaz, Ivan and Abbey, Craig K. and Timberg, Pontus and Eckstein, Miguel P. and Verdun, Francis R. and Castella, Cyril and Bochud, Francois O.}},
issn = {{1558-254X}},
keywords = {{Image quality assessment; model observers; optimization}},
language = {{eng}},
number = {{7}},
pages = {{1428--1435}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Medical Imaging}},
title = {{Derivation of an Observer Model Adapted to Irregular Signals Based on Convolution Channels}},
url = {{http://dx.doi.org/10.1109/TMI.2015.2395433}},
doi = {{10.1109/TMI.2015.2395433}},
volume = {{34}},
year = {{2015}},
}