The purposes of this research are to investigate the effectiveness
of our novel lung contour detection method in chest radiographs.
The proposed method consists of five sections as follows.
First, in order to reduce the amount of information, the images
are smoothed and subsampled from 2 k by 2.5 k pixels to 256 by
310 pixels with rescaling from 12-bit to 8-bit based on the image
maximum and minimum. Second, that the image is resolved into the
profiles for two directions (i.e., horizontal x and vertical y axes).
Then, for each direction, those profiles are tested by the neural network
which has been trained using the profiles from 14 pairs of
original and target images. Note that both horizontal and vertical
neural networks are trained with the horizontal and vertical profiles,
respectively. For each direction, the whole two-dimensional image is
reconstructed from the output profiles of the neural network. Next,
the binarization process followed by the labeling process is applied
to each reconstructed image individually. Finally, two postprocessed
images are combined through the OR operation, and the labeling
process is performed for the combined image to get the final contour.
A total of 85 screening chest radiographs from Johns Hopkins
University Hospital were digitized to 2 k by 2.5 k pixels with 12-bit
gray scale. Fourteen images were used for the training of the neural
networks and the remaining 71 images for testing. The proposed
method can detect the lung contour at 94% accuracy for test images
following the same rules as for the training images.