Pneumothorax detection in chest radiographs using local and global texture signatures

Ofer Geva; Gali Zimmerman-Moreno; Sivan Lieberman; Eli Konen; Hayit Greenspan

doi:10.1117/12.2083128

20 March 2015 Pneumothorax detection in chest radiographs using local and global texture signatures

Ofer Geva, Gali Zimmerman-Moreno, Sivan Lieberman, Eli Konen, Hayit Greenspan

Proceedings Volume 9414, Medical Imaging 2015: Computer-Aided Diagnosis; 94141P (2015) https://doi.org/10.1117/12.2083128
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

A novel framework for automatic detection of pneumothorax abnormality in chest radiographs is presented. The suggested method is based on a texture analysis approach combined with supervised learning techniques. The proposed framework consists of two main steps: at first, a texture analysis process is performed for detection of local abnormalities. Labeled image patches are extracted in the texture analysis procedure following which local analysis values are incorporated into a novel global image representation. The global representation is used for training and detection of the abnormality at the image level. The presented global representation is designed based on the distinctive shape of the lung, taking into account the characteristics of typical pneumothorax abnormalities. A supervised learning process was performed on both the local and global data, leading to trained detection system. The system was tested on a dataset of 108 upright chest radiographs. Several state of the art texture feature sets were experimented with (Local Binary Patterns, Maximum Response filters). The optimal configuration yielded sensitivity of 81% with specificity of 87%. The results of the evaluation are promising, establishing the current framework as a basis for additional improvements and extensions.

Citation Download Citation

Ofer Geva, Gali Zimmerman-Moreno, Sivan Lieberman, Eli Konen, and Hayit Greenspan "Pneumothorax detection in chest radiographs using local and global texture signatures", Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141P (20 March 2015); https://doi.org/10.1117/12.2083128

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