Accurate detection of microcalcification (MC) clusters is an important problem in breast cancer diagnosis. In this paper, we propose the use of a recently developed machine learning technique - relevance vector machine (RVM) - for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called relevance vectors. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier, which we developed previously. It is demonstrated that the RVM classifier achieves essentially the same detection performance as the SVM classifier, but does so with a much sparser kernel representation. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time implementation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Relevance vector machine learning for detection of microcalcifications in mammograms


    Contributors:


    Publication date :

    2005-01-01


    Size :

    128501 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Relevance Vector Machine Learning for Detection of Microcalcifications in Mammograms

    Wei, L. / Yang, Y. / Nishikawa, R. M. | British Library Conference Proceedings | 2005


    Detection of Microcalcifications in Digital Mammograms Images Using Wavelet Transform

    L., Cristina / Ponomaryov, Volodymyr / Sanchez R., J. | IEEE | 2006


    Computer Aided Detection of Clustered Microcalcifications in Digitized Mammograms Using Gabor Functions

    Catanzariti, E. / Ciminello, M. / Prevete, R. et al. | British Library Conference Proceedings | 2003



    Detection of microcalcifications in mammograms using error of prediction and statistical measures

    Acha, B. / Serrano, C. / Rangayyan, R.M. et al. | British Library Online Contents | 2009