Diagnosis of hip osteoarthritis is conventionally done through a manual measurement of the joint distance between the femoral head and the acetabular cup, a difficult and often error-prone process. Recently, Chen et al.1 proposed a fully automated technique based on landmark displacement estimation from multiple image patches that is able to accurately segment bone structures around the pelvis. This technique was shown to be comparable or better than state-of-the-art random forest based methods. In this paper, we report on the implementation and evaluation of this method on low-resolution datasets typically available in parts of the developing world where high-resolution X-ray image technology is unavailable.
We employed a dataset of hip joint images collected at a local clinic and provided to us in JPG format and at 1/3 the resolution of typical DICOM X-ray images. In addition, we employed the Dice similarity coefficient, average Euclidean distance between corresponding landmarks, and Hausdorff distance to better evaluate the method relative to diagnosis of hip osteoarthritis. Our results show that the proposed method is robust with JPEG images at 1/3 the resolution of DICOM data. Additional preliminary results quantify the accuracy of the approach as a function of decreasing resolution. We believe these results have important significance for application in clinical settings where modern X-ray equipment is not available.
Temporal subtraction techniques using 2D image registration improve the detectability of interval changes from chest
radiographs. Although such methods are well known for some time they are not widely used in radiologic practice. The
reason is the occurrence of strong pose differences between two acquisitions with a time interval of months to years in
between. Such strong perspective differences occur in a reasonable number of cases. They cannot be compensated by
available image registration methods and thus mask interval changes to be undetectable. In this paper a method is
proposed to estimate a 3D pose difference by the adaptation of a 3D rib cage model to both projections. The difference
between both is then compensated for, thus producing a subtraction image with virtually no change in pose. The method
generally assumes that no 3D image data is available from the patient. The accuracy of pose estimation is validated with
chest phantom images acquired under controlled geometric conditions. A subtle interval change simulated by a piece of
plastic foam attached to the phantom becomes visible in subtraction images generated with this technique even at strong
angular pose differences like an anterior-posterior inclination of 13 degrees.
KEYWORDS: Data modeling, Bone, Statistical modeling, Image segmentation, 3D modeling, Statistical analysis, Image processing, 3D image processing, Medical imaging, Process modeling
Statistical models of shape are a promising approach for robust and automatic segmentation of medical image
data. This work describes the construction of a statistical shape model of the pelvic bone. An interactive
approach is proposed for solving the correspondence problem which is able to handle shapes of arbitrary topology,
suitable for the genus 3 surface of the pelvic bone. Moreover it allows to specify corresponding anatomical features
as boundary constraints to the matching process. The model's capability for segmentation was tested on a set of
23 CT data sets. Quantitative results will be presented, showing that the model is well suited for segmentation
purposes.
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.