BAYESIAN FUSED CLASSIFICATION OF MEDICAL IMAGES

In many applications in computer vision and signal processing, it is n ecessary to assimilate data from multiple sources. This is a particula rly important issue in medical imaging, where information on a patient may be available from a number of different modalities. As a result, there has been much recent research interest in this area. We suggest an additional Bayesian method which generates a segmented classificati on concurrently with improving reconstructions of a set of registered images. A synthetic example is used to demonstrate the objectives and benefits of this proposed approach. Two medical. applications, one fus ing computed tomography (CT) and single photon emission computed tomog raphy (SPECT) brain scans, and the other magnetic resonance (MR) image s at two different resolutions, are considered.