The use of steerable channels for detecting asymmetrical signals with random orientations

In the optimization of medical imaging systems, there is a stringent need to shift from human observer studies to numerical observer studies, because of both cost and time limitations. Numerical models give an objective measure for the quality of displayed images for a given task and can be designed to predict the performance of medical specialists performing the same task. For the task of signal detection, the channelized Hotelling observer (CHO) has been successfully used, although several studies indicate an overefficiency of the CHO compared to human observers. One of the main causes of this overefficiency is attributed to the intrinsic uncertainty about the signal (such as its orientation) that a human observer is dealing with. Deeper knowledge of the discrepancies of the CHO and the human observer may provide extra insight in the processing of the human visual system and this knowledge can be utilized to better fine-tune medical imaging systems. In this paper, we investigate the optimal detection of asymmetrical signals with statistically known random orientation, based on joint detection and estimation theory. We derive the optimal channelized observer for this task and we show that the optimal detection in channel space requires the use of steerable channels, which are used in steerable pyramid transforms in image processing. Even though the use of CHOs for SKS tasks has not been studied so far, our findings indicate that CHO models can be further extended to incorporate intrinsic uncertainty about the signal to behave closer to humans. Experimental results are provided to illustrate these findings.