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Proceedings Paper

Pneumothorax detection in chest radiographs using local and global texture signatures
Author(s): Ofer Geva; Gali Zimmerman-Moreno; Sivan Lieberman; Eli Konen; Hayit Greenspan
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Paper 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.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9414, Medical Imaging 2015: Computer-Aided Diagnosis, 94141P (20 March 2015); doi: 10.1117/12.2083128
Show Author Affiliations
Ofer Geva, Tel Aviv Univ. (Israel)
Gali Zimmerman-Moreno, Tel Aviv Univ. (Israel)
Sivan Lieberman, Sheba Medical Ctr. (Israel)
Eli Konen, Sheba Medical Ctr. (Israel)
Hayit Greenspan, Tel Aviv Univ. (Israel)


Published in SPIE Proceedings Vol. 9414:
Medical Imaging 2015: Computer-Aided Diagnosis
Lubomir M. Hadjiiski; Georgia D. Tourassi, Editor(s)

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