Point process models for sweat gland activation observed with noise
Artikel i vetenskaplig tidskrift, 2021

The aim of this article is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors, we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors.

Bayesian inference

point pattern

soft-core inhibition

sequential point process

independent thinning

peripheral neuropathy

Författare

Mikko Kuronen

Naturresursinstitutet (Luke)

Mari Myllymaki

Naturresursinstitutet (Luke)

Adam Loavenbruck

University of Minnesota

Aila Särkkä

Göteborgs universitet

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Statistics in Medicine

0277-6715 (ISSN) 1097-0258 (eISSN)

Vol. In Press

Ämneskategorier

Sannolikhetsteori och statistik

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling

DOI

10.1002/sim.8891

PubMed

33517587

Mer information

Senast uppdaterat

2021-02-22