NCC-based stitching performance in MFP framework

Abstract

In clinics, long-length medical images such as lower limb or whole spine are required for diagnostic purposes. Because of limited x-ray detector panel size, long-length images should be acquired by stitching of multiple images with minimum overlap area. To find the stitching point, similarity is measured in overlapped area between two serial images by calculating normalized cross correlation (NCC). Typically, medical image stitching technique introduces a lead ruler as a marker for stitching. However, marker-based stitching can result in discordance of anatomical structures near stitching line when there is a patient movement during sequential image acquisition. Therefore, anatomical-structure-based stitching is the ideal method. Nevertheless, scattering of x-rays and noise from detector panel decrease the similarity of overlapped area from two serial images, which can lead to erroneous stitching. In this study, we suggested the introduction of multi-frequency processing (MFP) framework for anatomical-structure-based-stitching to reduce the stitching error.

To reduce the calculation time, fast Fourier transform (FFT) was used. Sample images for NCC calculation were selected on the overlapped region. MFP was applied for these selected samples to get edged enhanced images. Using MFP applied samples, NCC calculation was carried out to determine stitching point. After determinant of stitching point, seam-line is eliminated using triangulation average blending.

NCC-based stitching in MFP framework outperformed the simple NCC-based stitching. Stitching in MFP framework significantly reduces the possibility of erroneous stitching.

Introduction of MFP before NCC calculation greatly increased the performance of stitching. Anatomical-structure-based stitching without a lead ruler can be successfully achieved without visible error.