A comparative study on microcalcification detection methods with posterior probability estimation based on Gaussian mixture models

TitleA comparative study on microcalcification detection methods with posterior probability estimation based on Gaussian mixture models
Publication TypeConference Proceedings
Year of Conference2005
AuthorsCasaseca-de-la-Higuera, P., J. I. Arribas, E. Muñoz-Moreno, and C. Alberola-Lopez
Conference NameEngineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the
Pagination49–54
PublisherIEEE
Abstract

Automatic detection of microcalcifications in mammograms constitutes a helpful tool in breast cancer diagnosis. Radiologist's confidence level on microcalcification detection would be improved if a probability estimate of its presence could be obtained from computer-aided diagnosis. In this paper we explore detection performance of a simple Bayesian classifier based on Gaussian mixture probability density functions (pdf). Posterior probability of microcalcification presence may be estimated from the probabilistic model. Two model selection algorithms have been tested, one based on the minimum message length criterion and the other on discriminative criteria obtained from the classifier performance. In addition, we propose a complementing model selection algorithm in order to improve the initial system performance obtained with these methods. Simulation results show that our model gets a good compromise between classification performance and probability estimation accuracy

URLhttps://ieeexplore.ieee.org/abstract/document/1616339
DOI10.1109/IEMBS.2005.1616339