  In this paper, new methods are considered to detect knee joint areas in
bilateral PA fixed flexion knee X-ray images. The methods are of template
matching type where the distance criterion is based on the negative normalized
cross-correlation. The manual annotations are made on only one side of a single
bilateral image when the templates are selected. The best matching patch search
is formulated as an unconstrained continuous domain minimization problem. For
the minimization problem different optimization methods are considered. The
main method of the paper is a trainable optimizer where the method is taught to
take zoomed and possibly rotated patches from its input images which look like
the template. In the experiments, we compare the minimum values found by
different optimization methods. We also look at some test images to examine the
correspondence between the minimum value and how well the knee area is
localized. It seems that making annotations only to a single image enables to
detect knee joint areas quite precisely.
