Mr. Joel M. Witten Profile

Joel M. Witten

at Univ of Maryland College Park

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 13 March 2009 Paper

Using partial least squares to compute efficient channels for the Bayesian ideal observer

Joel Witten, Subok Park, Kyle Myers

Proceedings Volume 7263, 72630Q (2009) https://doi.org/10.1117/12.813550

KEYWORDS: Image quality, Imaging systems, Binary data, Algorithm development, Medical imaging, Image analysis, Signal detection, Computing systems, Receivers, Imaging devices

Read Abstract +

We define image quality by how accurately an observer, human or otherwise, can perform a given task, such as determining to which class an image belongs. For detection tasks, the Bayesian ideal observer is the best observer, in that it sets an upper bound for observer performance, summarized by the area under the receiver operating characteristic curve. However, the use of this observer is frequently infeasible because of unknown image statistics, whose estimation is computationally costly. As a result, a channelized ideal observer (CIO) was investigated to reduce the dimensionality of the data, yet approximate the performance of the ideal observer. Previously investigated channels include Laguerre Gauss (LG) channels and channels via the singular value decomposition of the given linear system (SVD). Though both types are highly efficient for the ideal observer, they nevertheless have the weakness that they may not be as efficient for general detection tasks involving complex/realistic images; the former is particular to the signal and background shape, and the latter is particular to the system operator. In this work, we attempt to develop channels that can be applied to a system with any signal and background type and without knowledge of any characteristics of the system. The method used is a partial least squares algorithm (PLS), in which channels are chosen to maximize the squared covariance between images and their classes. Preliminary results show that the CIO with PLS channels outperforms one with either the LG or SVD channels and very closely approximates ideal-observer performance.

Proceedings Article | 24 March 2008 Paper

Singular vectors of a linear imaging system as efficient channels for the ideal observer in detection tasks involving non-Gaussian distributed lumpy images

Joel Witten, Subok Park, Kyle Myers

Proceedings Volume 6917, 69170U (2008) https://doi.org/10.1117/12.772054

KEYWORDS: Imaging systems, Signal detection, Binary data, Medical imaging, Image quality, Interference (communication), Diagnostics, Algorithm development, Data modeling, Matrices

Read Abstract +

The Bayesian ideal observer sets an upper bound for diagnostic performance of an imaging system in binary detection tasks. Thus, this observer should be used for image quality assessment whenever possible. However, it is difficult to compute ideal-observer performance because the probability density functions of the data, required for the observer, are often unknown in tasks involving complex backgrounds. Furthermore, the dimension of the integrals that need to be calculated for the observer is huge. To attempt to reduce the dimensionality of the problem, and yet still approximate ideal-observer performance, a channelized-ideal observer (CIO) with Laguerre-Gauss channels was previously investigated for detecting a Gaussian signal at a known location in non-Gaussian lumpy images. While the CIO with Laguerre-Gauss channels had, in some cases, approximated ideal-observer performance, there was still a gap between the mean performance of the ideal observer and the CIO. Moreover, it is not clear how to choose efficient channels for the ideal observer. In the current work, we investigate the use of singular vectors of a linear imaging system as efficient channels for the ideal observer in the same tasks. Singular value decomposition of the imaging system is performed to obtain its singular vectors. Singular vectors most relevant to the signal and background images are chosen as candidate channels. Results indicate that the singular vectors are not only more efficient than Laguerre-Gauss channels, but are also highly efficient for the ideal observer. The results further demonstrate that singular vectors strongly associated with the signal-only image are the most efficient channels.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years