Feature space and metric measures for fusing multisensor images
International Journal of Remote Sensing
Image dissimilarity-based quantification of lung disease from CT
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Multiple-instance learning as a classifier combining problem
Pattern Recognition
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This work presents general signal detection schemes based on the static fusion of posterior probabilities. Starting with the assumption that for every pixel in an image there is a posterior probability-indicating the probability of the presence or the absence of the signal to be detected, some well-known probability fusion schemes and generalizations thereof are proposed to come to an overall decision regarding the presence or absence of the signal. In addition to these well-known static fusion schemes-i.e.,voting, averaging, maximum rule, etcetera, a quantile-based combination rule is presented as well. The performance of the several rules is evaluated on two real-world, medical image analysis task. Both tasks consider the computer-aided diagnosis (CAD) of standard posteroanterior chest radiographs. More specifically, in the first task the general detection of interstitial diseases is studied, while in the second task the focus is on the detection of tuberculosis.