Medical image registration using machine learning-based interest point detector

Sergey Sergeev; Yang Zhao; Marius George Linguraru; Kazunori Okada

doi:10.1117/12.911717

14 February 2012 Medical image registration using machine learning-based interest point detector

Sergey Sergeev, Yang Zhao, Marius George Linguraru, Kazunori Okada

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 831424 (2012) https://doi.org/10.1117/12.911717
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

This paper presents a feature-based image registration framework which exploits a novel machine learning (ML)-based interest point detection (IPD) algorithm for feature selection and correspondence detection. We use a feed-forward neural network (NN) with back-propagation as our base ML detector. Literature on ML-based IPD is scarce and to our best knowledge no previous research has addressed feature selection strategy for IPD purpose with cross-validation (CV) detectability measure. Our target application is the registration of clinical abdominal CT scans with abnormal anatomies. We evaluated the correspondence detection performance of the proposed ML-based detector against two well-known IPD algorithms: SIFT and SURF. The proposed method is capable of performing affine rigid registrations of 2D and 3D CT images, demonstrating more than two times better accuracy in correspondence detection than SIFT and SURF. The registration accuracy has been validated manually using identified landmark points. Our experimental results shows an improvement in 3D image registration quality of 18.92% compared with affine transformation image registration method from standard ITK affine registration toolkit.

Citation Download Citation

Sergey Sergeev, Yang Zhao, Marius George Linguraru, and Kazunori Okada "Medical image registration using machine learning-based interest point detector", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831424 (14 February 2012); https://doi.org/10.1117/12.911717

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