Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach

Kristians Diaz; Benjamin Castaneda

doi:10.1117/12.770965

26 March 2008 Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach

Kristians Diaz, Benjamin Castaneda

Author Affiliations +

Proceedings Volume 6914, Medical Imaging 2008: Image Processing; 69144A (2008) https://doi.org/10.1117/12.770965
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

This paper presents a semi-automated algorithm for prostate boundary segmentation from three-dimensional (3D) ultrasound (US) images. The US volume is sampled into 72 slices which go through the center of the prostate gland and are separated at a uniform angular spacing of 2.5 degrees. The approach requires the user to select four points from slices (at 0, 45, 90 and 135 degrees) which are used to initialize a discrete dynamic contour (DDC) algorithm. 4 Support Vector Machines (SVMs) are trained over the output of the DDC and classify the rest of the slices. The output of the SVMs is refined using binary morphological operations and DDC to produce the final result. The algorithm was tested on seven ex vivo 3D US images of prostate glands embedded in an agar mold. Results show good agreement with manual segmentation.

Citation Download Citation

Kristians Diaz and Benjamin Castaneda "Semi-automated segmentation of the prostate gland boundary in ultrasound images using a machine learning approach", Proc. SPIE 6914, Medical Imaging 2008: Image Processing, 69144A (26 March 2008); https://doi.org/10.1117/12.770965

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available