We are developing a minimal path searching method for active shape model (ASM)-based segmentation for detection
of lung boundaries on digital radiographs. With the conventional ASM method, the position and shape parameters of the
model points are iteratively refined and the target points are updated by the least Mahalanobis distance criterion. We
propose an improved searching strategy that extends the searching points in a fan-shape region instead of along the
normal direction. A minimal path (MP) deformable model is applied to drive the searching procedure. A statistical shape
prior model is incorporated into the segmentation. In order to keep the smoothness of the shape, a smooth constraint is
employed to the deformable model. To quantitatively assess the ASM-MP segmentation, we compare the automatic
segmentation with manual segmentation for 72 lung digitized radiographs. The distance error between the ASM-MP and
manual segmentation is 1.75 ± 0.33 pixels, while the error is 1.99 ± 0.45 pixels for the ASM. Our results demonstrate
that our ASM-MP method can accurately segment the lung on digital radiographs.
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.