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.
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.