Paper
9 March 2010 Rib suppression in chest radiographs to improve classification of textural abnormalities
Laurens E. Hogeweg, Christian Mol, Pim A. de Jong, Bram van Ginneken
Author Affiliations +
Abstract
The computer aided diagnosis (CAD) of abnormalities on chest radiographs is difficult due to the presence of overlapping normal anatomy. Suppression of the normal anatomy is expected to improve performance of a CAD system, but such a method has not yet been applied to the computer detection of interstitial abnormalities such as occur in tuberculosis (TB). The aim of this research is to evaluate the effect of rib suppression on a CAD system for TB. Profiles of pixel intensities sampled perpendicular to segmented ribs were used to create a local PCA-based shape model of the rib. The model was normalized to the local background intensity and corrected for gradients perpendicular to the rib. Subsequently rib suppressed images were created by subtracting the models for each rib from the original image. The effect of rib suppression was evaluated using a CAD system for TB detection. Small square image patches were sampled randomly from 15 normal and 35 TB-affected images containing textural abnormalities. Abnormalities were outlined by a radiologist and were given a subtlety rating from 1 to 5. Features based on moments of intensity distributions of Gaussian derivative filtered images were extracted. A supervised learning approach was used to discriminate between normal and diseased image patches. The use of rib suppressed images increased the overall performance of the system, as measured by the area under the receiver operator characteristic (ROC) curve, from 0.75 to 0.78. For the more subtly rated patches (rated 1-3) the performance increased from 0.62 to 0.70.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Laurens E. Hogeweg, Christian Mol, Pim A. de Jong, and Bram van Ginneken "Rib suppression in chest radiographs to improve classification of textural abnormalities", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76240Y (9 March 2010); https://doi.org/10.1117/12.844409
Lens.org Logo
CITATIONS
Cited by 11 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Chest imaging

CAD systems

Computer aided diagnosis and therapy

Solid modeling

Current controlled current source

Gaussian filters

Image filtering

Back to Top