X-ray image classification using Random Forests with Local Binary Patterns

This paper presents a novel algorithm for the efficient classification of X-ray images to enhance the accuracy and performance. As for describing the characteristics of X-ray image, new Local Binary Patterns (LBP) is employed that allows simple and efficient feature extraction for texture informatio...

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Bibliographic Details
Main Authors: Seong-Hoon Kim, Ji-Hyun Lee, Byoungchul Ko, Jae-Yeal Nam
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
Classification algorithms
Feature extraction
Histograms
Image classification
Local Binary Patterns
Random Forests
Training
X-ray image classification
X-ray imaging
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Summary:This paper presents a novel algorithm for the efficient classification of X-ray images to enhance the accuracy and performance. As for describing the characteristics of X-ray image, new Local Binary Patterns (LBP) is employed that allows simple and efficient feature extraction for texture information. To achieve fast and accurate classification task, Random Forests that is decision tree based ensemble classifier is applied. Comparing with other feature descriptors and classifiers, the testing results show that the proposed method improves accuracy, especially the speed for either training or testing.
ISSN:2160-133X
DOI:10.1109/ICMLC.2010.5580711