Extending the channelized Hotelling observer to account for signal uncertainty and estimation tasks

In medicine, images are taken so that specific tasks can be performed. Thus, any measure of image quality must account for the task the images are to be used for and the observer performing the task. Performing task-based optimizations using human observers is generally difficult, time consuming, expensive and, in the case of hardware optimizations, not necessarily ideal. Model observers have been successfully used in place of human observers. The channelized Hotelling observer is one such model observer. Depending on the choice of channels, the channelized Hotelling observer can be used to either predict human-observer performance or as an ideal observer. This paper will focus on the use of the channelized Hotelling observer as an approximation of the ideal linear observer. Laguerre Gauss channels have proven useful for ideal-observer computations, but these channels are somewhat limited because they require the signal to be known exactly both in terms of location and shape. In fact, the Laguerre Gauss channels require the signal to be radially symmetric. We have devised a new method of determining efficient channels that does not require the signal to be symmetric and can even account for signal variability. This method can even be used for linear estimation tasks. We have compared the performances of the channelized Hotelling observer using both this new set of channels and the Laguerre Gauss channels for a signal-known-exactly detection task, and found that they correlate.