HoDOr: histogram of differential orientations for rigid landmark tracking in medical images

Abhishek Tiwari; Kedar Anil Patwardhan

doi:10.1117/12.2293083

2 March 2018 HoDOr: histogram of differential orientations for rigid landmark tracking in medical images

Abhishek Tiwari, Kedar Anil Patwardhan

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057419 (2018) https://doi.org/10.1117/12.2293083
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Abhishek Tiwari and Kedar Anil Patwardhan "HoDOr: histogram of differential orientations for rigid landmark tracking in medical images", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057419 (2 March 2018); https://doi.org/10.1117/12.2293083

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