Intra-class and inter-class tool discrimination through micro-CT analysis of false starts on bone

Giraudo, Chiara; Montisci, Massimo; Giorgetti, Arianna; Martinuzzo, Lisa; Bisceglia, Marco; Moschi, Sofia; Fais, Paolo; Weber, Michael; Quaia, Emilio; Viel, Guido; Cecchetto, Giovanni

doi:10.1007/s00414-019-02157-3

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Particularly, micro-CT analysis of false starts (Fig. 3D) produced by different hand saws on human bones allowed the correct coupling of tool marks and classes of saws [35,36] and also the differentiation of false starts produced by saws belonging to the same class [37], with a high level of accuracy and precision [38]. Moreover, a random forest statistical regression model was proposed to enable prediction of saw blade thickness from empirical data [39] and 3D printed models deriving from micro-CT scans were used in forensic cases of dismemberment to detect several cut marks and false starts and identify the tools used by the murderer [40].…”

Section: Tool Markmentioning

confidence: 99%

Forensic applications of micro-computed tomography: a systematic review

Franchetti

Viel

Fais

et al. 2022

Clin Transl Imaging

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Purpose The aim of this systematic review was to provide a comprehensive overview of micro-CT current applications in forensic pathology, anthropology, odontology, and neonatology. Methods A bibliographic research on the electronic databases Pubmed and Scopus was conducted in the time frame 01/01/2001–31/12/2021 without any language restrictions and applying the following free-text search strategy: “(micro-computed tomography OR micro-CT) AND (forensic OR legal)”. The following inclusion criteria were used: (A) English language; (B) Application of micro-CT to biological and/or non-biological materials to address at least one forensic issue (e.g., age estimation, identification of post-mortem interval). The papers selected by three independent investigators have been then classified according to the investigated materials. Results The bibliographic search provided 651 records, duplicates excluded. After screening for title and/or abstracts, according to criteria A and B, 157 full-text papers were evaluated for eligibility. Ninety-three papers, mostly (64) published between 2017 and 2021, were included; considering that two papers investigated several materials, an overall amount of 99 classifiable items was counted when referring to the materials investigated. It emerged that bones and cartilages (54.55%), followed by teeth (13.13%), were the most frequently analyzed materials. Moreover, micro-CT allowed the collection of structural, qualitative and/or quantitative information also for soft tissues, fetuses, insects, and foreign materials. Conclusion Forensic applications of micro-CT progressively increased in the last 5 years with very promising results. According to this evidence, we might expect in the near future a shift of its use from research purposes to clinical forensic cases.

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Section: Tool Markmentioning

confidence: 99%

Forensic applications of micro-computed tomography: a systematic review

Franchetti

Viel

Fais

et al. 2022

Clin Transl Imaging

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“…Gong et al proposed to maximize interclass variance and minimize intraclass variance to calculate the dispersion ratio and filtered out the best feature subsets to improve model performance [25]. Chiara et al calculated the intraclass and interclass distance to obtain the dispersion ratio of the micro-CT samples, and the damage categories were distinguished by the dispersion ratio [26]. Then, the distance measurement weight is obtained by computing the ReliefF distance.…”

Section: Introductionmentioning

confidence: 99%

An Improved Feature Parameter Extraction Algorithm of Composite Detection Method Based on the Fusion Theory

et al. 2021

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An improved feature parameter extraction algorithm is proposed in this study to solve the problem of quantitative detection of subsurface defects. Firstly, the common feature parameters from the differential signal of pulsed eddy current and ultrasonic are extracted in time domain and frequency domain. Then, the dispersion model and ReliefF model are established to determine the weights of each parameter. Finally, the weights from the two different algorithms are fused by the D-S evidence theory to determine feature parameters. Compared with the PCA feature parameter algorithm from the pulsed eddy current or ultrasonic, the experiment results show the feature parameters extracted by the algorithm proposed in this paper are more effective in quantitative detection of subsurface defects. It will lead to high accuracy in the subsurface defections.

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