Edge-preserving adaptive autoregressive model for Poisson noise reduction.

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Reijo Takalo, Heli Hytti, Heimo Ihalainen, Antti Sohlberg

Author Information

Reijo Takalo: Division of Nuclear Medicine, Department of Diagnostic Radiology, Oulu University Hospital, Oulu.
Heli Hytti: Gastroenterology Outpatient Clinic, Tampere University Hospital, Tampere.
Heimo Ihalainen: Faculty of Engineering and Natural Sciences, Automation Technology and Mechanical Engineering, Tampere University, Tampere.
Antti Sohlberg: Laboratory of Clinical Physiology and Nuclear Medicine, Joint Authority for P��ij��t-H��me Social and Health Care, Lahti, Finland.

PMID: 33560719 DOI: 10.1097/MNM.0000000000001377

Autoregressive models in image processing are linear prediction models that split an image into a predicted (i.e. filtered) image and a prediction error image, which extracts data on the image edges. Edge separation is a crucial feature of an autoregressive model. Data on the edges can be processed in different ways and then added to the filtered image. Another basic feature of our method is spatially varying modelling. In this short article, we propose an improved autoregressive model that preserves image sharpness around the edges of the image and focus on the reduction of Poisson noise, which degrades nuclear medicine images and presents a special challenge in medical imaging.

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Poisson Distribution

Image Processing, Computer-Assisted

Signal-To-Noise Ratio

Regression Analysis

Humans

Journal Article

No available data.

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