Point process models for sweat gland activation observed with noise
Journal article, 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.

soft-core inhibition

Bayesian inference

independent thinning

point pattern

peripheral neuropathy

sequential point process


Mikko Kuronen

Natural Resources Institute Finland (Luke)

Mari Myllymaki

Natural Resources Institute Finland (Luke)

Adam Loavenbruck

University of Minnesota

Aila Särkkä

University of Gothenburg

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Statistics in Medicine

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

Vol. 40 8 2055-2072

Subject Categories

Probability Theory and Statistics

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing





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