Automatic detection of direct radiation for digital fluoroscopy optimization
We present a histogram-based real-time solution to detecting directly irradiated regions in digital fluoroscopic images. Our method leverages the power of model matching, machine learning and domain knowledge to characterize and segment images using histograms. The input image is automatically identified as containing partial, all, or null direct radiation. The regions with direct radiation are segmented out via global thresholding according to image characterizations. The algorithm involves only one-dimensional processing. The test results achieved 99.82% accurate detection rate on a dataset of 9256 clinical images.