KEYWORDS: Image segmentation, Digital filtering, Image filtering, Gaussian filters, Mammography, Image quality, Denoising, Breast cancer, Image processing algorithms and systems
Digital mammography is a valuable technique for breast cancer detection, because it is safe, noninvasive and can reduce unnecessary biopsies. However, it is difficult to distinguish masses from normal or dense regions because of their morphological characteristics and ambiguous margins. Thus, improvement of image quality, highlighting the tissues details and performing mass segmentation are important tasks for early breast cancer diagnosis. This work presents a mini-Mammographic Image Analysis Society (MIAS) database preprocessing, system which combines classic and efficient techniques of Median, Wiener and Gaussian filters to remove salt and pepper, speckle and gaussian noise in mammography images. The experimental results indicates that the Gaussian filter outperforms other filtering techniques, as shown by evaluated by Peak Signal to Noise Ratio and Mean Square Error metrics.
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.