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12 May 2004 Automated detection and classification of interstitial lung diseases from low-dose CT images
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Abstract
We developed a computer-aided diagnosis (CAD) scheme to detect and quantitatively assess interstitial lung diseases (ILD) depicted on low-dose and multi-slice helical high-resolution computed tomography (CT) examinations. Eighteen CT cases acquired from patients who underwent routine low-dose whole-lung screening examinations for the detection of lung cancer were used to test the scheme. ILD was identified in all of these cases. The CAD scheme involves multiple steps to segment lung areas, identify suspicious ILD regions depicted on each CT slice, and generate volumetric ILD lesions by grouping and matching ILD regions detected on multiple adjacent slices. The scheme computes five “global” features for each identified ILD region, which include size (or volume), contrast, average local pixel value fluctuation, mean of stochastic fractal dimension, and geometric fractal dimension. Two sets of classification rules are applied to remove false-positive detections. The severity of ILD in each case was rated by one experienced chest radiologist into one of the three categories (mild, moderate, and severe). A distance-weighted k-nearest neighbor algorithm and round-robin validation method was applied to classify each testing case into one of the three categories of severity. In this experiment, the CAD scheme classified 78% (14 out of 18) cases into the same categories as rated by the radiologist.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bin Zheng, Joseph Ken Leader, Carl R. Fuhrman, Frank C. Sciurba, and David Gur "Automated detection and classification of interstitial lung diseases from low-dose CT images", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); https://doi.org/10.1117/12.533774
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