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X-ray Image Classification Using Random Forests with Local Wavelet-Based CS-Local Binary Patterns.

This paper presents a fast and efficient method for classifying X-ray images using random forests with proposed local wavelet-based local binary pattern (LBP) to improve image classification performance and reduce training and testing time. Most studies on local binary patterns and its modifications...

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Published in:Journal of Digital Imaging Vol. 24; no. 6; pp. 1141 - 1152
Main Authors: Ko, Byoung, Kim, Seong, Nam, Jae-Yeal
Format: diagnostic images equations & formulas research tables/charts Journal Article
Published: Springer Nature Dec2011
Online Access:View this record in EBSCOhost
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      dt: Dec2011
      vid: 24
      iid: 6
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-011-9380-3
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        atl: X-ray Image Classification Using Random Forests with Local Wavelet-Based CS-Local Binary Patterns.
      aug:
        au:
          Ko, Byoung
          Kim, Seong
          Nam, Jae-Yeal
        affil: Department of Computer Engineering, Keimyung University, Shindang-dong Dalseo-gu Daegu 704-701 South Korea
      sug:
        subj:
          X-Rays Classification
          Decision Support Techniques
          Human
          Automation
          Algorithms
          Comparative Studies
          Validation Studies
      ab: This paper presents a fast and efficient method for classifying X-ray images using random forests with proposed local wavelet-based local binary pattern (LBP) to improve image classification performance and reduce training and testing time. Most studies on local binary patterns and its modifications, including centre symmetric LBP (CS-LBP), focus on using image pixels as descriptors. To classify X-ray images, we first extract local wavelet-based CS-LBP (WCS-LBP) descriptors from local parts of the images to describe the wavelet-based texture characteristic. Then we apply the extracted feature vector to decision trees to construct random forests, which are an ensemble of random decision trees. Using the random forests with local WCS-LBP, we classified one test image into the category having the maximum posterior probability. Compared with other feature descriptors and classifiers, the proposed method shows both improved performance and faster processing time.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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