Lung texture classification using bag of visual words

18 March 2014 Lung texture classification using bag of visual words

Marina Asherov, Idit Diamant, Hayit Greenspan

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 90352K (2014) https://doi.org/10.1117/12.2044162
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

Interstitial lung diseases (ILD) refer to a group of more than 150 parenchymal lung disorders. High-Resolution Computed Tomography (HRCT) is the most essential imaging modality of ILD diagnosis. Nonetheless, classification of various lung tissue patterns caused by ILD is still regarded as a challenging task. The current study focuses on the classification of five most common categories of lung tissues of ILD in HRCT images: normal, emphysema, ground glass, fibrosis and micronodules. The objective of the research is to classify an expert-given annotated region of interest (AROI) using a bag of visual words (BoVW) framework. The images are divided into small patches and a collection of representative patches are defined as visual words. This procedure, termed dictionary construction, is performed for each individual lung texture category. The assumption is that different lung textures are represented by a different visual word distribution. The classification is performed using an SVM classifier with histogram intersection kernel. In the experiments, we use a dataset of 1018 AROIs from 95 patients. Classification using a leave-one-patient-out cross validation (LOPO CV) is used. Current classification accuracy obtained is close to 80%.

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Marina Asherov, Idit Diamant, and Hayit Greenspan "Lung texture classification using bag of visual words", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90352K (18 March 2014); https://doi.org/10.1117/12.2044162

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