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.
|Title of host publication||Medical Imaging 2015|
|Subtitle of host publication||Computer-Aided Diagnosis|
|Editors||Lubomir M. Hadjiiski, Georgia D. Tourassi|
|State||Published - 2015|
|Event||SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis - Orlando, United States|
Duration: 22 Feb 2015 → 25 Feb 2015
|Name||Progress in Biomedical Optics and Imaging - Proceedings of SPIE|
|Conference||SPIE Medical Imaging Symposium 2015: Computer-Aided Diagnosis|
|Period||22/02/15 → 25/02/15|
Bibliographical notePublisher Copyright:
© 2015 SPIE.
- Chest radiography
- Computer-aided diagnosis
- Supervised classifier