Novel motion detection algorithm of 3D medical data for diagnosis purposes

Open Access
Conference Proceedings
Authors: Martin ŽagarAlan MutkaHung Tai

Abstract: The encoding and streaming of medical video imaging for diagnosis purposes is an example of high computational capacity applications. Motion detection presents one of the pillars in encoding such videos and a basis for compression where just motion vectors are encoding instead of the complete frame. We propose a new algorithm for motion detection of 3D medical videos where the novel approach is based on the fact that medical videos don’t have sudden and large motions. We defined an adaptive medical search algorithm that starts with presumptions that the motion is coherent for acquired medical 3D MRI or CT data.Currently in the H265/HEVC encoding standard (the most recent standard for video compression), in sense of accuracy, only the Full search algorithm for motion estimation of medical data analysis is applicable, so there is a need for implementation of other, more efficient algorithms. While motion estimation algorithms for general-purpose algorithms (where input video can have any possible motion features) are well known (triangle, pentagon, and square pattern), those for medical applications without sudden and large motions are not yet optimized which is very relevant in sense of speed needed for calculations, when we consider typical 500MB MRI datasets.Our algorithm is presenting an entry point of a bigger framework for the compression of multidimensional medical data. In these terms, a list of potential customers includes any users of medical data visualization systems that seek high accuracy and fast computation for multidimensional medical data visualization, for example, in applications in OR during surgeries, preoperative planning, and diagnostics, that might be used by a wide variety of medical specialists currently using regular DICOM viewers with inefficient rendering. We plan to offer our algorithm to producers of the DICOM viewers to optimize their calculations for visualizations, to speed them up.Compared to the Full search algorithm, in our algorithm, the initial search pattern is checking just 23 points, one center point, 14 checking points that surround the center (at points with absolute Manhattan distance equals 2), and additional 8 points with spatial vectors |i|=|j|=|k|=1, in order to obtain maximum accuracy for initial motion vector whereas there is a tradeoff for the further calculations. Framework next predicts motion vector for all other consecutive frames based on minimum block distortion for two possible blocks – the one which is predicted by the previous motion vector and the new one re-calculated with our algorithm.We see an opportunity in the context of updating image viewers for diagnosis purposes that are currently present on the market (like DICOM viewers), so the computations of motion detections will be much faster and equally accurate as when using the Full search algorithm. The main advantage would be time reduction when running such applications that would raise the user experience (which might be important for medical specialists in OR) and reduce the cost of the equipment needed to run such applications (because of less computational complexity it will need less computer power).In this paper, we will present our research outcomes, main benefits, and possible risks in implementation, and also describe how our novel algorithm for motion detection of 3D medical videos is highly efficient while the same accuracy as currently used full search algorithm for motion detection in 3D medical data videos.

Keywords: Motion Estimation, 3D Medical Imaging, Visualization And Diagnosis

DOI: 10.54941/ahfe100910

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