Spectral Embedding Based Probabilistic Boosting Tree (ScEPTre): Classifying High Dimensional Heterogeneous Biomedical Data

  • Pallavi Tiwari
  • Mark Rosen
  • Galen Reed
  • John Kurhanewicz
  • Anant Madabhushi
Conference paper

DOI: 10.1007/978-3-642-04271-3_102

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)
Cite this paper as:
Tiwari P., Rosen M., Reed G., Kurhanewicz J., Madabhushi A. (2009) Spectral Embedding Based Probabilistic Boosting Tree (ScEPTre): Classifying High Dimensional Heterogeneous Biomedical Data. In: Yang GZ., Hawkes D., Rueckert D., Noble A., Taylor C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg

Abstract

The major challenge with classifying high dimensional biomedical data is in identifying the appropriate feature representation to (a) overcome the curse of dimensionality, and (b) facilitate separation between the data classes. Another challenge is to integrate information from two disparate modalities, possibly existing in different dimensional spaces, for improved classification. In this paper, we present a novel data representation, integration and classification scheme, Spectral Embedding based Probabilistic boosting Tree (ScEPTre), which incorporates Spectral Embedding (SE) for data representation and integration and a Probabilistic Boosting Tree classifier for data classification. SE provides an alternate representation of the data by non-linearly transforming high dimensional data into a low dimensional embedding space such that the relative adjacencies between objects are preserved. We demonstrate the utility of ScEPTre to classify and integrate Magnetic Resonance (MR) Spectroscopy (MRS) and Imaging (MRI) data for prostate cancer detection. Area under the receiver operating Curve (AUC) obtained via randomized cross validation on 15 prostate MRI-MRS studies suggests that (a) ScEPTre on MRS significantly outperforms a Haar wavelets based classifier, (b) integration of MRI-MRS via ScEPTre performs significantly better compared to using MRI and MRS alone, and (c) data integration via ScEPTre yields superior classification results compared to combining decisions from individual classifiers (or modalities).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pallavi Tiwari
    • 1
  • Mark Rosen
    • 2
  • Galen Reed
    • 3
  • John Kurhanewicz
    • 3
  • Anant Madabhushi
    • 1
  1. 1.Department of Biomedical EngineeringRutgers UniversityUSA
  2. 2.Department of RadiologyUniversity of PennsylvaniaUSA
  3. 3.Department of RadiologyUniversity of CaliforniaSan FranciscoUSA

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