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Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM. [ABSTRACT FROM AUTHOR]

Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM.

This paper addresses the visualization of complex information using multidimensional scaling (MDS). MDS is a technique adopted for processing data with multiple features scattered in high-dimensional spaces. For illustrating the proposed techniques, the case of viral diseases is considered. The study evaluates the characteristics of 21 viruses in the perspective of clinical information. Several new schemes are proposed for improving the visualization of the MDS charts. The results follow standard clinical practice, proving that the method represents a valuable tool to study a large number of viruses. [ABSTRACT FROM AUTHOR]

Treating time as the third dimension of 2D time-dependent flow enables the application of a wide variety of visualization techniques for 3D stationary vector fields. In the resulting space-time representation, 3D streamlines represent 2D pathlines of the original field. In this paper, we investigate the application of different streamline-based visualization concepts to the 3D space-time representation of 2D time-dependent flow. As a consequence, we obtain from each streamline-based concept a Galilean-invariant counterpart that takes the time dependence of the original field explicitly into account. We show the advantages of the overall approach for vortex analysis and the analysis of the dynamics of material lines. In particular, we employ the concept for the extraction of vortex centers, vortex core regions, and the visualization of material line dynamics using streamsurface integration and line integral convolution in the space-time field. We exemplify the utility of our visualization approach using two 2D time-dependent datasets that exhibit vortical flow. Graphical Abstract: [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]