arXiv subjects

Seung-Mo Hong

Publications and source records attributed to Seung-Mo Hong.

4 recordsLinked to original sources

arXiv2026

Quantitative three-dimensional absorption imaging in standard brightfield microscopes

Optical absorption is a primary, label-defining contrast across biology, pathology, and materials science, yet three-dimensional quantitative absorption imaging has remained largely inaccessible to the brightfield microscopes used in everyday practice. We introduce quantitative absorption tomography (QAT), which recovers volumetric distributions of the extinction coefficient by treating brightfield image formation as a linear inverse problem in logarithmic intensity space and inverting a three-dimensional absorption optical transfer function. Under weak-scattering conditions, QAT yields spectrally resolved, three-dimensional absorption maps from through-focus image stacks acquired on standard brightfield platforms, without interferometry, coherent illumination, or sample rotation. We use QAT to track melanin dynamics in living melanoma cells without exogenous labels, image pigment organization in intact Petunia hybrida petals in vivo, and reconstruct chromogenic contrast across large H&E-stained human tissue volumes. By establishing absorption as a directly measurable volumetric quantity within standard brightfield workflows, QAT positions chromogenic contrast as a quantitative axis alongside fluorescence- and refractive-index-based imaging.

physics.optics↗

arXiv2025

Incoherent dielectric tensor tomography for quantitative 3D measurement of biaxial anisotropy

Biaxial anisotropy, arising from distinct optical responses along three principal directions, underlies the complex structure of many crystalline, polymeric, and biological materials. However, existing techniques such as X-ray diffraction and electron microscopy require specialized facilities or destructive preparation and cannot provide full three-dimensional (3D) information. Here we introduce incoherent dielectric tensor tomography (iDTT), a non-interferometric optical imaging method that quantitatively reconstructs the 3D dielectric tensor under incoherent, polarization-diverse illumination. By combining polarization diversity and angular-spectrum modulation, iDTT achieves speckle-free and vibration-robust mapping of biaxial birefringence with submicron resolution. Simulations and experiments on uniaxial and biaxial samples validate its quantitative accuracy. Applied to mixed and polycrystalline materials, iDTT distinguishes crystal types by their birefringent properties and reveals 3D grain orientations and boundaries. This approach establishes iDTT as a practical and accessible tool for quantitative, label-free characterization of biaxial anisotropy in diverse materials.

physics.optics↗

arXiv2014

K-Adaptive Partitioning for Survival Data, with an Application to Cancer Staging

In medical research, it is often needed to obtain subgroups with heterogeneous survivals, which have been predicted from a prognostic factor. For this purpose, a binary split has often been used once or recursively; however, binary partitioning may not provide an optimal set of well separated subgroups. We propose a multi-way partitioning algorithm, which divides the data into K heterogeneous subgroups based on the information from a prognostic factor. The resulting subgroups show significant differences in survival. Such a multi-way partition is found by maximizing the minimum of the subgroup pairwise test statistics. An optimal number of subgroups is determined by a permutation test. Our developed algorithm is compared with two binary recursive partitioning algorithms. In addition, its usefulness is demonstrated with a real data of colorectal cancer cases from the Surveillance Epidemiology and End Results program. We have implemented our algorithm into an R package maps, which is freely available in the Comprehensive R Archive Network (CRAN).

stat.AP↗

arXiv2014

Identification of Outlying Observations with Quantile Regression for Censored Data

Outlying observations, which significantly deviate from other measurements, may distort the conclusions of data analysis. Therefore, identifying outliers is one of the important problems that should be solved to obtain reliable results. While there are many statistical outlier detection algorithms and software programs for uncensored data, few are available for censored data. In this article, we propose three outlier detection algorithms based on censored quantile regression, two of which are modified versions of existing algorithms for uncensored or censored data, while the third is a newly developed algorithm to overcome the demerits of previous approaches. The performance of the three algorithms was investigated in simulation studies. In addition, real data from SEER database, which contains a variety of data sets related to various cancers, is illustrated to show the usefulness of our methodology. The algorithms are implemented into an R package OutlierDC which can be conveniently employed in the \proglang{R} environment and freely obtained from CRAN.

stat.CO↗