Automatic image analysis for detecting and quantifying gamma-ray sources in coded-aperture images

Author

Schaich, Paul C. ; Clark, Gregory A. ; Sengupta, Sailes K. ; Ziock, Klaus-Peter

Author_Institution

Lawrence Livermore Nat. Lab., CA, USA

Volume

43

Issue

4

fYear

1996

fDate

8/1/1996 12:00:00 AM

Firstpage

2419

Lastpage

2426

Abstract

We report the development of an automatic image analysis system that detects gamma-ray source regions in images obtained from a coded aperture, gamma-ray imager. The number of gamma sources in the image is not known prior to analysis. The system counts the number (K) of gamma sources detected in the image and estimates the lower bound for the probability that the number of sources in the image is K. The system consists of a two-stage pattern classification scheme in which the probabilistic neural network is used in the supervised learning mode. The algorithms were developed and tested using real gamma-ray images from controlled experiments in which the number and location of depleted uranium source disks in the scene are known. The novelty of the work lies in the creative combination of algorithms and the successful application of the algorithms to real images of gamma-ray sources

Keywords

detector circuits; gamma-ray detection; high energy physics instrumentation computing; image classification; learning (artificial intelligence); neural nets; nuclear electronics; radioactive sources; automatic image analysis; coded-aperture images; depleted uranium source disks; gamma-ray source detection; probabilistic neural network; real images; supervised learning mode; two-stage pattern classification scheme; Apertures; Cameras; Gamma ray detection; Gamma ray detectors; Image analysis; Image processing; Laboratories; Nuclear imaging; Pattern recognition; Probability;

fLanguage

English

Journal_Title

Nuclear Science, IEEE Transactions on

Publisher

ieee

ISSN

0018-9499

Type

jour

DOI

10.1109/23.531791

Filename

531791