As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
In order to detect prostate diseases, urologists usually use ultrasound images or magnetic resonance images (MRI) for clinical diagnosis. In clinical practice, region of prostate is manually outlined by urologist. However, outline the prostate boundary manually is highly time-consuming. In this paper, a prostate segmentation and volume estimation in MRI is proposed. Anactive contour model(ACM) is adopted to obtain the initial contour of the prostate. Four textural features extracted from the prostate were used to train the SVM classifier. Non-prostate regions are then excluded by the trained SVM. A quick convex hull is applied to refine the shape of prostate. The volume of the prostate is eventually estimated by the series of segmented prostate regions. The proposed segmentation method achieves high accuracy of 93.7%. Our experimental results show that the proposed prostate segmentation and volume estimation method is highly potential for helping urologists in clinical diagnosis.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.