The vertebral pose is critical information in orthopedics. An automated vertebral pose estimation can provide direct supports to medical diagnoses. In this paper, we proposed a vertebral pose estimation based on the given two sets of training patterns. The first set contains the images of vertebrae, in which all vertebral columns are fixed at a proper pose; the second are the images which are cropped with arbitrarily shift and rotation. Based on these two pattern sets, the proposed method can perform template matching. By using exhaustive searching, we will be able to estimate the poses of the vertebral columns on the given x-ray images. We propose a new approach for extracting critical information from the given training patterns in the problems of classification. In this work, we use it to estimate the poses of vertebral columns on x-ray images. The proposed method consists of two parts: 1, feature extraction and 2, classification. the first part extracts the unique features from the two given training pattern sets. These unique features are used to support the second part, which is a classifier inspired by the famous AdaBoost.
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