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Activity Number: 26 - Imaging Speed Session
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Section on Statistics in Imaging
Abstract #318642
Title: A New Correlation-Based Method for Co-Localization Analysis in Super-Resolution Images
Author(s): Xueyan Liu* and Clifford Guy and Emilio Boada Romero and Douglas Green and Cheng Cheng and Hui Zhang
Companies: University of New Orleans and St. Jude Children's Research Hospital and St. Jude Children's Research Hospital and St. Jude Children's Research Hospital and St. Jude Children’s Research Hospital and Division of Biostatistics, Northwestern University
Keywords: Co-localization; spatial statistics; microscopic images; super resolution; R

Super-resolution imaging techniques have significantly improved the resolution of microscopic images from 250 to a few nanometers over the past decades. In the meanwhile, new coordinate-based statistical methods using precise locational information to study co-localization of dual-color spatial points in super-resolution microscopy images are in high demand. In this study, we propose a new Pearson's correlation-based method, named Normalized Spatial Intensity Correlation (NSInC, “in-synch”), to quantify the degree of co-localization at interested or dynamic proximity scales with precise edge effects correction which removes biasedness in the current available methods. Simulations and real applications are provided for illustrating the validity and applicability.

Authors who are presenting talks have a * after their name.

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