Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

Noriji Kato; Motofumi Fukui; Takashi Isozaki

doi:10.1117/12.810976

27 February 2009 Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

Noriji Kato, Motofumi Fukui, Takashi Isozaki

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72600C (2009) https://doi.org/10.1117/12.810976
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

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.

Citation Download Citation

Noriji Kato, Motofumi Fukui, and Takashi Isozaki "Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72600C (27 February 2009); https://doi.org/10.1117/12.810976

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