Biometric Recognition of Personality based on Spiral Computed Tomography Data

Nechyporenko, Alina; Alekseeva, Victoriia; Nazaryan, R. S.; Gargin, Vitaliy

doi:10.1109/cadsm52681.2021.9385267

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…However, fluorescence studies together with computer vision systems, artificial intelligence and telesurgery for the evaluation of pathomorphological images do not dispense the diagnostic gold standards of benign or malignant tumors (conventional examination, biopsy, ultrasonography, and computed tomography, etc.) [ [72] , [73] , [74] , [75] , [76] , [77] , [78] ].…”

Section: New Fluorescent Agents Of Molecular Actionmentioning

confidence: 99%

Fluorescence visualization for cancer DETECTION: EXPERIENCE and perspectives

Kravchenko,

Sikora,

Wireko

et al. 2024

Heliyon

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Section: New Fluorescent Agents Of Molecular Actionmentioning

confidence: 99%

Fluorescence visualization for cancer DETECTION: EXPERIENCE and perspectives

Kravchenko,

Sikora,

Wireko

et al. 2024

Heliyon

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Automatic personal identification using a single CT image

Heinrich

2024

Eur Radiol

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Objectives Computer vision (CV) mimics human vision, enabling computers to automatically compare radiological images from recent examinations with a large image database for unique identification, crucial in emergency scenarios involving unknown patients or deceased individuals. This study aims to extend a CV-based personal identification method from orthopantomograms (OPGs) to computed tomography (CT) examinations using single CT slices. Methods The study analyzed 819 cranial computed tomography (CCT) examinations from 722 individuals, focusing on single CT slices from six anatomical regions to explore their potential for CV-based personal identification in 69 procedures. CV automatically identifies and describes interesting features in images, which can be recognized in a reference image and then designated as matching points. In this study, the number of matching points was used as an indicator for identification. Results Across six different regions, identification rates ranged from 41/69 (59%) to 69/69 (100%) across over 700 possible identities. Comparison of images from the same individual achieved higher matching points, averaging 6.32 ± 0.52% (100% represents the maximum possible matching points), while images of different individuals averaged 0.94 ± 0.15%. Reliable matching points are found in the teeth, maxilla, cervical spine, skull bones, and paranasal sinuses, with the maxillary sinuses and ethmoidal cells being particularly suitable for identification due to their abundant matching points. Conclusion Unambiguous identification of individuals based on a single CT slice is achievable, with maxillary sinus CT slices showing the highest identification rates. However, metal artifacts, especially from dental prosthetics, and various head positions can hinder identification. Clinical relevance statement Radiology possesses a multitude of reference images for a CV database, facilitating automated CV-based personal identification in emergency examinations or cases involving unknown deceased individuals. This enhances patient care and communication with relatives by granting access to medical history. Key Points Unknown individuals in radiology or forensics pose challenges, addressed through automatic CV-based identification methods. A single CT slice highlighting the maxillary sinuses is particularly effective for personal identification. Radiology plays a pivotal role in automated personal identification by leveraging its extensive image database.

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Biometric Recognition of Personality based on Spiral Computed Tomography Data

Cited by 2 publications

References 26 publications

Fluorescence visualization for cancer DETECTION: EXPERIENCE and perspectives

Fluorescence visualization for cancer DETECTION: EXPERIENCE and perspectives

Automatic personal identification using a single CT image

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