Abstract
This paper explores a computer-aided detection
scheme to aid radiologists in making a higher percentage of
correct diagnoses when analysing chest radiographs. The
approach undertaken in the detection process is to use several
proprietary image processing algorithms to adjust, segment and
classify a radiograph. Firstly, a Difference of Gaussian (DoG)
energy normalisation method is applied to the image. By doing
this, the effect of differing equipment and calibrations is
normalised. Thereafter, the lung area is detected using Active
Shape Models (ASMs). Once identified, the lungs are analysed
using Local Binary Patterns (LBPs). This technique is combined
with a probability measure that makes use of the the locations of
known abnormalities in the training dataset. The results of the
segmentation when compared to ground truth masks achieves an
overlap segmentation accuracy of 87,598±3,986%. The
challenges faced during classification are also discussed.