Kernel Generalized-Gaussian Mixture Model for Robust Abnormality Detection

  • Nitin Kumar
  • Ajit V. Rajwade
  • Sharat Chandran
  • Suyash P. AwateEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


Typical methods for abnormality detection in medical images rely on principal component analysis (PCA), kernel PCA (KPCA), or their robust invariants. However, typical robust-KPCA methods use heuristics for model fitting and perform outlier detection ignoring the variances of the data within principal subspaces. In this paper, we propose a novel method for robust statistical learning by extending the multivariate generalized-Gaussian distribution to a reproducing kernel Hilbert space and employing it within a mixture model. We propose expectation maximization to fit our kernel generalized-Gaussian mixture model (KGGMM), using solely the Gram matrix and without the explicit lifting map. We exploit the KGGMM, including component means, principal directions, and variances, for abnormality detection in images. The results on 4 large publicly available datasets, involving retinopathy and cancer, show that our method outperforms the state of the art.


Abnormality detection One-class classification Kernel methods Robustness Generalized gaussian Mixture model Expectation maximization 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nitin Kumar
    • 1
  • Ajit V. Rajwade
    • 1
  • Sharat Chandran
    • 1
  • Suyash P. Awate
    • 1
    Email author
  1. 1.Computer Science and Engineering DepartmentIndian Institute of Technology (IIT) BombayMumbaiIndia

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