A Bag-of-Features Approach to Classify Six Types of Pulmonary Textures on High-Resolution Computed Tomography

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Author(s)

    • XU Rui
    • the Graduate School of Medicine, Yamaguchi University
    • TACHIBANA Rie
    • the Information Science and Technology Dept., Oshima National College of Maritime Technology
    • KIDO Shoji
    • the Graduate School of Medicine, Yamaguchi University

Abstract

Computer-aided diagnosis (CAD) systems on diffuse lung diseases (DLD) were required to facilitate radiologists to read high-resolution computed tomography (HRCT) scans. An important task on developing such CAD systems was to make computers automatically recognize typical pulmonary textures of DLD on HRCT. In this work, we proposed a bag-of-features based method for the classification of six kinds of DLD patterns which were consolidation (CON), ground-glass opacity (GGO), honeycombing (HCM), emphysema (EMP), nodular (NOD) and normal tissue (NOR). In order to successfully apply the bag-of-features based method on this task, we focused to design suitable local features and the classifier. Considering that the pulmonary textures were featured by not only CT values but also shapes, we proposed a set of statistical measures based local features calculated from both CT values and eigen-values of Hessian matrices. Additionally, we designed a support vector machine (SVM) classifier by optimizing parameters related to both kernels and the soft-margin penalty constant. We collected 117 HRCT scans from 117 subjects for experiments. Three experienced radiologists were asked to review the data and their agreed-regions where typical textures existed were used to generate 3009 3D volume-of-interest (VOIs) with the size of 32×32×32. These VOIs were separated into two sets. One set was used for training and tuning parameters, and the other set was used for evaluation. The overall recognition accuracy for the proposed method was 93.18%. The precisions/sensitivities for each texture were 96.67%/95.08% (CON), 92.55%/94.02% (GGO), 97.67%/99.21% (HCM), 94.74%/93.99% (EMP), 81.48%/86.03%(NOD) and 94.33%/90.74% (NOR). Additionally, experimental results showed that the proposed method performed better than four kinds of baseline methods, including two state-of-the-art methods on classification of DLD textures.

Journal

  • IEICE Transactions on Information and Systems

    IEICE Transactions on Information and Systems 96(4), 845-855, 2013-04-01

    The Institute of Electronics, Information and Communication Engineers

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