Channelized detection filters for detecting tumors in nuclear medical images

The problem of detecting known objects of known location in the presence of stationary noise is well understood, the solution being the prewhitening matched filter. Detecting tumors in nuclear medical images presents a more challenging problem: the object being a mass whose shape and location are not known exactly, and the background anatomy being nonstationary. This paper addresses the latter problem by using simulated images to train a channelized detection algorithm. We show that the detector converges to the prewhitening matched filter providing the signal is known and the noise is stationary. We report on the manner in which the detector departs from the matched filter under more realistic conditions. Using detectability da as the performance measure, this method is tested on simulated tumors in simulated anatomical backgrounds. Results show that for certain channel configurations, the channelized detection filters perform equivalently to the prewhitening matched filter. High detectability is maintained using a limited number of channels and in cases in which the tumor size varies.