A Comparative Study of 2D UMI and 3D Zernike Shape Descriptor for ATS Drugs Identification

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

doi:10.1007/978-3-319-17398-6_22

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…On the other hand, the features are tested in terms of classification accuracy against well-known classifier, Random Forest (RF) [77] from WEKA Machine Learning package [78]. RF is employed in this study, because previous studies conducted by [51], [79]- [81] have found that RF is the most suitable for the molecular structure data. In this study, the number of trees employed by RF is 165, equals to the number of attributes of all 3D moments.…”

Section: Methodsmentioning

confidence: 99%

Using 3D Hahn Moments as A Computational Representation of ATS Drugs Molecular Structure

Pratama

Muda

Choo

et al. 2020

Advances in Intelligent Systems and Computing

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The campaign against drug abuse is fought by all countries, most notably on ATS drugs. The technical limitations of the current test kits to detect new brand of ATS drugs present a challenge to law enforcement authorities and forensic laboratories. Meanwhile, new molecular imaging devices which allowed mankind to characterize the physical 3D molecular structure have been recently introduced, and it can be used to remedy the limitations of existing drug test kits. Thus, a new type of 3D molecular structure representation technique should be developed to cater the 3D molecular structure acquired physically using these molecular imaging devices. One of the applications of image processing methods to represent a 3D image is 3D moments, and this study formulates a new 3D moments technique, namely 3D Hahn moments, to represent the 3D molecular structure of ATS drugs. The performance of the proposed technique was analysed using drug chemical structures obtained from UNODC for the ATS drugs, while non-ATS drugs are obtained randomly from ChemSpider database. The evaluation shows the technique is qualified to be further explored in the future works to be fully compatible with ATS drug identification domain.

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Section: Methodsmentioning

confidence: 99%

Using 3D Hahn Moments as A Computational Representation of ATS Drugs Molecular Structure

Pratama

Muda

Choo

et al. 2020

Advances in Intelligent Systems and Computing

Self Cite

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“…The 3D Zernike descriptors have been successfully applied to protein structural similarity retrieval ( Mak et al, 2008 ; Sael et al, 2008 ), protein-protein docking using region-based ( Venkatraman et al, 2009b ), terrain matching ( Ye and Chen, 2012 ), and amphetamine-type stimulant (ATS) drugs identification ( Pratama et al, 2015 ). The maximum order of 3D Zernike moments used in most, if not all, studies is below 30 and can only represent the rough shape features of 3D objects because the existing computation method developed by Novotni and Klein is time-consuming and computationally instable for calculating higher orders of Zernike moments ( Zhang et al, 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth

Gwo

Zhu²,

Zhang³

2022

Front. Neurosci.

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Analyses of age-related white matter hyperintensity (WMH) lesions manifested in T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) have been mostly on understanding the size and location of the WMH lesions and rarely on the morphological characterization of the lesions. This work extends our prior analyses of the morphological characteristics and texture of WMH from 2D to 3D based on 3D T2 FLAIR images. 3D Zernike transformation was used to characterize WMH shape; a fuzzy logic method was used to characterize the lesion texture. We then clustered 3D WMH lesions into groups based on their 3D shape and texture features. A potential growth index (PGI) to assess dynamic changes in WMH lesions was developed based on the image texture features of the WMH lesion penumbra. WMH lesions with various sizes were segmented from brain images of 32 cognitively normal older adults. The WMH lesions were divided into two groups based on their size. Analyses of Variance (ANOVAs) showed significant differences in PGI among WMH shape clusters (P = 1.57 × 10–3 for small lesions; P = 3.14 × 10–2 for large lesions). Significant differences in PGI were also found among WMH texture group clusters (P = 1.79 × 10–6). In conclusion, we presented a novel approach to characterize the morphology of 3D WMH lesions and explored the potential to assess the dynamic morphological changes of WMH lesions using PGI.

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2D and 3D Image Analysis by Moments

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A Comparative Study of 2D UMI and 3D Zernike Shape Descriptor for ATS Drugs Identification

Cited by 4 publications

References 35 publications

Using 3D Hahn Moments as A Computational Representation of ATS Drugs Molecular Structure

Using 3D Hahn Moments as A Computational Representation of ATS Drugs Molecular Structure

Brain white matter hyperintensity lesion characterization in 3D T2 fluid-attenuated inversion recovery magnetic resonance images: Shape, texture, and their correlations with potential growth

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