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Computer-Aided Detection of Polyps in CT Colonography with Pixel-Based Machine Learning Techniques

  • Jian-Wu Xu
  • Kenji Suzuki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Pixel/voxel-based machine-learning techniques have been developed for classification between polyp regions of interest (ROIs) and non-polyp ROIs in computer-aided detection (CADe) of polyps in CT colonography (CTC). Although 2D/3D ROIs can be high-dimensional, they may reside in a lower dimensional manifold. We investigated the manifold structure of 2D CTC ROIs by use of the Laplacian eigenmaps technique. We compared a support vector machine (SVM) classifier with the Laplacian eigenmaps-based dimensionality-reduced ROIs with massive-training support vector regression (MTSVR) in reduction of false positive (FP) detections. The Laplacian eigenmaps-based SVM classifier removed 16.0% (78/489) of FPs without any loss of polyps in a leave-one-lesion-out cross-validation test, whereas the MTSVR removed 49.9% (244/489); thus, yielded a 96.6% by-polyp sensitivity at an FP rate of 2.4 (254/106) per patient.

Keywords

colorectal cancer computer-aided detection manifold learning pixel-based machine learning support vector machines 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jian-Wu Xu
    • 1
  • Kenji Suzuki
    • 1
  1. 1.Department of RadiologyThe University of ChicagoChicago

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