X-ray image classification using Random Forests with Local Binary Patterns
Seong‐Hoon Kim, Ji-Hyun Lee, Byoung Chul Ko, Jae-Yeal Nam · 2010
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.