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.