A comparative study of 2D UMI and 3D zernike shape descriptor for ATS drugs identification

Satrya Fajri Pratama, Azah Kamilah Muda, Yun Huoy Choo, Ajith Abraham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drug abuse is a threat to national development. Generally, drugs can be identified based on the structure of its molecular components. This procedure is becoming more unreliable with the introduction of new amphetamine-type stimulants (ATS) molecular structures which are increasingly complex and sophisticated. An in-depth study is crucial to accurately identify the unique characteristics of molecular structure in ATS drug. Therefore, this chapter is meant for exploring the usage of shape descriptors (SD) to represent the drug molecular structure. Twodimensional (2D) united moment invariant (UMI) and three-dimensional (3D) Zernike are selected and their performances are analyzed using drug chemical structures obtained from United Nations Office of Drugs and Crime (UNODC) and various sources. The evaluation identifies the most interesting method to be further explored and adapted in the future work to fully compatible with ATS drug identification domain.

Original languageEnglish
Title of host publicationPattern Analysis, Intelligent Security and the Internet of Things
EditorsAjith Abraham, Azah Kamilah Muda, Yun-Huoy Choo
PublisherSpringer Nature
Pages237-249
Number of pages13
ISBN (Print)9783319173979
DOIs
Publication statusPublished - 2015
Event4th World Congress on Information and Communication Technologies, WICT 2014 - Malacca, Malaysia
Duration: 8 Dec 201411 Dec 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume355
ISSN (Print)2194-5357

Conference

Conference4th World Congress on Information and Communication Technologies, WICT 2014
Country/TerritoryMalaysia
CityMalacca
Period8/12/1411/12/14

Keywords

  • 3D Zernike
  • ATS drugs
  • Drugs identification
  • Molecular structure
  • Moment invariants function
  • United moment invariant

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