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Proceedings Paper

Medical image registration using machine learning-based interest point detector
Author(s): Sergey Sergeev; Yang Zhao; Marius George Linguraru; Kazunori Okada
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Paper 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.

Paper Details

Date Published: 14 February 2012
PDF: 7 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 831424 (14 February 2012); doi: 10.1117/12.911717
Show Author Affiliations
Sergey Sergeev, San Francisco State Univ. (United States)
Yang Zhao, San Francisco State Univ. (United States)
Marius George Linguraru, Children's National Medical Ctr. (United States)
Kazunori Okada, San Francisco State Univ. (United States)


Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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