Image texture analysis plays an important role in object detection and recognition in image processing. The texture
analysis can be used for early detection of breast cancer by classifying the mammogram images into normal and
abnormal classes. This study investigates breast cancer detection using texture features obtained from the grey level cooccurrence
matrices (GLCM) of curvelet sub-band levels combined with texture feature obtained from the image itself.
The GLCM were constructed for each sub-band of three curvelet decomposition levels. The obtained feature vector
presented to the classifier to differentiate between normal and abnormal tissues. The proposed method is applied over
305 region of interest (ROI) cropped from MIAS dataset. The simple logistic classifier achieved 86.66% classification
accuracy rate with sensitivity 76.53% and specificity 91.3%.
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.