Body part recognition based on CT slice images is very important for many applications in PACS and CAD systems. In
this paper, we propose a novel approach that can recognize which body part a slice image belongs to robustly. We focus
on how to effectively express and use the unique statistical information of the correlation between the CT value and the
position information of each body part. We apply the machine learning method AdaBoost to express and use this
statistical information. Our approach consists of a training process and a recognition process. In the training process, we
first define the whole body using a set of specific classes to ensure that training images in the same class have a high
similarity, and prepare a training image set (positive samples and negative samples) for each class. Second, the training
images are normalized to a fixed size and rotation in each class. Third, features are calculated for each normalized
training image. Finally, AdaBoosted histogram classifiers are trained. After the training process, each class has its own
classifiers. In the recognition process, given a series of CT images, the scores of all classes for each slice image are calculated based on the classifiers obtained in the training process. Then, based on the scores of each slice and a simple model of body part sequence continuity, we use dynamic programming (DP) to eliminate false recognition results. Experimental results on 440 unknown series including lesions show that our approach has high a recognition rate.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.