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Novel Pattern Recognition Method for Analysis the Radiation Exposure in Cancer Treatment

  • Dmitriy DubovitskiyEmail author
  • Valeri Kouznetsov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 881)

Abstract

A novel pattern recognition technique has been deployed in the treatment of cancer tumours to provide improved targeting of ionising radiation and more accurate measurement of the radiation dose. The radiation beams enter the body from different directions to concentrate on the tumour. The centre of the tumour has to be precisely located relatively to patient’s skin surface, so the radiation does not affect healthy tissue and produces successful treatment. Existing methods of 3D dose measurement are highly labor-intensive and generally suffer from low accuracy. In this publication, we propose a new method of 3D measurement of the dose in real-time by using skin pattern recognition technology. The textural pattern of the patient’s skin is analysed from an image sensor in a specially designed camera using Fractal Geometry and Fuzzy logic. A specially designed net sensor is then placed over the area of skin exposed to the treatment in order to measure the radiation dose. The algorithms discussed below enable the precise focussing of the radiation. The novel object recognition technique provides a mathematical tool to build a volume model of the dose distribution inside the patient’s body. This paper provides an overview and specific information on the technology and necessary background for future industrial implementation into health care infrastructure.

Keywords

Cancer treatment In vivo dosimetry Radiation sensors Pattern analysis Decision making Object recognition Image morphology Image recognition Pattern recognition Texture classification Computational geometry 

Notes

Acknowledgement

The work reported in this paper is supported by the Oxford Recognition Ltd. The authors are grateful to Richard Spooner, Ann Wallace and Gladys O’Brien for help in the preparation of this paper.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Oxford Recognition LtdCambridgeUK

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