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Proceedings Paper

HoDOr: histogram of differential orientations for rigid landmark tracking in medical images
Author(s): Abhishek Tiwari; Kedar Anil Patwardhan
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Paper Abstract

Feature extraction plays a pivotal role in pattern recognition and matching. An ideal feature should be invariant to image transformations such as translation, rotation, scaling, etc. In this work, we present a novel rotation-invariant feature, which is based on Histogram of Oriented Gradients (HOG). We compare performance of the proposed approach with the HOG feature on 2D phantom data, as well as 3D medical imaging data. We have used traditional histogram comparison measures such as Bhattacharyya distance and Normalized Correlation Coefficient (NCC) to assess efficacy of the proposed approach under effects of image rotation. In our experiments, the proposed feature performs 40%, 20%, and 28% better than the HOG feature on phantom (2D), Computed Tomography (CT-3D), and Ultrasound (US-3D) data for image matching, and landmark tracking tasks respectively.

Paper Details

Date Published: 2 March 2018
PDF: 7 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057419 (2 March 2018); doi: 10.1117/12.2293083
Show Author Affiliations
Abhishek Tiwari, Samsung R&D Institute (India)
Kedar Anil Patwardhan, Samsung R&D Institute (India)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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