Proceedings Article | 17 March 2008
KEYWORDS: Lung, Chest, Computer aided diagnosis and therapy, Opacity, Emphysema, Chromium, Absorbance, Chest imaging, Image segmentation, CAD systems
We have developed a new computer-aided diagnostic (CAD) scheme for detection of diffuse lung disease in computed
radiographic (CR) chest images. One hundred ninety-four chest images (56 normals and 138 abnormals with diffuse
lung diseases) were used. The 138 abnormal cases were classified into three levels of severity (34 mild, 60 moderate,
and 44 severe) by an experienced chest radiologist with use of five different patterns, i.e., reticular, reticulonodular,
nodular, air-space opacity, and emphysema. In our computerized scheme, the first moment of the power spectrum, the
root-mean-square variation, and the average pixel value were determined for each region of interest (ROI), which was
selected automatically in the lung fields. The average pixel value and its dependence on the location of the ROI were
employed for identifying abnormal patterns due to air-space opacity or emphysema. A rule-based method was used for
determining three levels of abnormality for each ROI (0: normal, 1: mild, 2: moderate, and 3: severe). The distinction
between normal lungs and abnormal lungs with diffuse lung disease was determined based on the fractional number of
abnormal ROIs by taking into account the severity of abnormalities. Preliminary results indicated that the area under the
ROC curve was 0.889 for the 44 severe cases, 0.825 for the 104 severe and moderate cases, and 0.794 for all cases. We
have identified a number of problems and reasons causing false positives on normal cases, and also false negatives on
abnormal cases. In addition, we have discussed potential approaches for improvement of our CAD scheme. In
conclusion, the CAD scheme for detection of diffuse lung diseases based on texture features extracted from CR chest
images has the potential to assist radiologists in their interpretation of diffuse lung diseases.
