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In this paper, new methods are considered to detect knee joint areas inbilateral PA fixed flexion knee X-ray images. The methods are of templatematching type where the distance criterion is based on the negative normalizedcross-correlation. The manual annotations are made on only one side of a singlebilateral image when the templates are selected. The best matching patch searchis formulated as an unconstrained continuous domain minimization problem. Forthe minimization problem different optimization methods are considered. Themain method of the paper is a trainable optimizer where the method is taught totake zoomed and possibly rotated patches from its input images which look likethe template. In the experiments, we compare the minimum values found bydifferent optimization methods. We also look at some test images to examine thecorrespondence between the minimum value and how well the knee area islocalized. It seems that making annotations only to a single image enables todetect knee joint areas quite precisely.

A Neural Template Matching Method to Detect Knee Joint Areas