Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize
texture analysis approaches with second and higher order statistics, and show successful classification result among
various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture
distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can
only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome
this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives,
which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature
Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power
due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance
under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the
local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes
(ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system
achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level
co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.
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