Program Information
BEST IN PHYSICS (IMAGING): Effect of Image Processing Parameters On Nodule Detectability in Chest Radiography
K Little*, Z Lu , H MacMahon , I Reiser , University of Chicago, Chicago, IL
Presentations
WE-G-204-1 (Wednesday, July 15, 2015) 4:30 PM - 6:00 PM Room: 204
Purpose: To investigate the effect of varying system image processing parameters on lung nodule detectability in digital radiography.
Methods: An anthropomorphic chest phantom was imaged in the posterior-anterior position using a GE Discovery XR656 digital radiography system. To simulate lung nodules, a polystyrene board with 6.35mm diameter PMMA spheres was placed adjacent to the phantom (into the x-ray path). Due to magnification, the projected simulated nodules had a diameter in the radiographs of approximately 7.5 mm. The images were processed using one of GE’s default chest settings (Factory3) and reprocessed by varying the “Edge” and “Tissue Contrast” processing parameters, which were the two user-configurable parameters for a single edge and contrast enhancement algorithm. For each parameter setting, the nodule signals were calculated by subtracting the chest-only image from the image with simulated nodules. Twenty nodule signals were averaged, Gaussian filtered, and radially averaged in order to generate an approximately noiseless signal. For each processing parameter setting, this noise-free signal and 180 background samples from across the lung were used to estimate ideal observer performance in a signal-known-exactly detection task. Performance was estimated using a channelized Hotelling observer with 10 Laguerre-Gauss channel functions.
Results: The “Edge” and “Tissue Contrast” parameters each had an effect on the detectability as calculated by the model observer. The CHO-estimated signal detectability ranged from 2.36 to 2.93 and was highest for “Edge” = 4 and “Tissue Contrast” = -0.15. In general, detectability tended to decrease as “Edge” was increased and as “Tissue Contrast” was increased. A human observer study should be performed to validate the relation to human detection performance.
Conclusion: Image processing parameters can affect lung nodule detection performance in radiography. While validation with a human observer study is needed, model observer detectability for common tasks could provide a means for optimizing image processing parameters.
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