  The following paper proposes two contour-based fracture detection schemes.
The development of the contour-based fracture is based on the line-based
fracture detection schemes proposed in arXiv:1902.07458. Existing Computer
Aided Diagnosis (CAD) systems commonly employs Convolutional Neural Networks
(CNN), although the cost to obtain a high accuracy is the amount of training
data required. The purpose of the proposed schemes is to obtain a high
classification accuracy with a reduced number of training data through the use
of detected contours in X-ray images. There are two contour-based fracture
detection schemes. The first is the Standard Contour Histogram Feature-Based
(CHFB) and the second is the improved CHFB scheme. The difference between the
two schemes is the removal of the surrounding detected flesh contours from the
leg region in the improved CHFB scheme. The flesh contours are automatically
classified as non-fractures. The contours are further refined to give a precise
representation of the image edge objects. A total of 19 features are extracted
from each refined contour. 8 out of the 19 features are based on the number of
occurrences for particular detected gradients in the contour. Moreover, the
occurrence of the 0-degree gradient in the contours are employed for the
separation of the knee, leg and foot region. The features are a summary
representation of the contour, in which it is used as inputs into the
Artificial Neural Network (ANN). Both Standard CHFB and improved CHFB schemes
are evaluated with the same experimental set-ups. The average system accuracy
for the Standard CHFB scheme is 80.7%, whilst the improved CHFB scheme has an
average accuracy of 82.98%. Additionally, the hierarchical clustering technique
is adopted to highlight the fractured region within the X-ray image, using
extracted 0-degree gradients from fractured contours.
