  Cephalometric analysis is an important tool for orthodontic diagnosis. At
present, most cephalometric analysis is performed with the help of image
processing techniques. Hence, the resolution between millimeter and pixel is
needed with high accuracy. In cephalometric analysis, a scale is placed in
front of patient's head when taking the radiograph. This study aims to develop
an algorithm to recognize the scale in cephalogram, locate the scale mark and
calculate the pixel-millimeter-ratio. First, a ROI is detected and cropped
based on regression tree voting. Second, an algorithm is employed in ROI to
detect the corner points of the scale and rotate the scale to perfectly
vertical direction. Finally, a pixel tracing algorithm is employed to locate
the first and the last scale mark in order to calculate the pixel length of the
calibration. A novel method is proposed to adaptively assign the size and
orientation of the image patches described by the SIFT vectors to ensure
invariance of scale and rotation of images. The algorithm is robust to
interference including tags, stickers and stain. A dataset consisting 163
cephalograms is tested and the algorithm performs a 100% Success Detection Rate
within the precision range of 1.0mm.
