Literature DB >> 18069044

Preventing facial recognition when rendering MR images of the head in three dimensions.

François Budin1, Donglin Zeng, Arpita Ghosh, Elizabeth Bullitt.   

Abstract

In the United States it is not allowed to make public any patient-specific information without the patient's consent. This ruling has led to difficulty for those interested in sharing three-dimensional (3D) images of the head and brain since a patient's face might be recognized from a 3D rendering of the skin surface. Approaches employed to date have included brain stripping and total removal of the face anterior to a cut plane, each of which lose potentially important anatomical information about the skull surface, air sinuses, and orbits. This paper describes a new approach that involves (a) definition of a plane anterior to which the face lies, and (b) an adjustable level of deformation of the skin surface anterior to that plane. On the basis of a user performance study using forced choices, we conclude that approximately 30% of individuals are at risk of recognition from 3D renderings of unaltered images and that truncation of the face below the level of the nose does not preclude facial recognition. Removal of the face anterior to a cut plane may interfere with accurate registration and may delete important anatomical information. Our new method alters little of the underlying anatomy and does not prevent effective registration into a common coordinate system. Although the methods presented here were not fully effective (one subject was consistently recognized under the forced choice study design even at the maximum deformation level employed) this paper may point a way toward solution of a difficult problem that has received little attention in the literature.

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Mesh:

Year:  2007        PMID: 18069044      PMCID: PMC2504704          DOI: 10.1016/j.media.2007.10.008

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  2 in total

1.  The role of eyebrows in face recognition.

Authors:  Javid Sadr; Izzat Jarudi; Pawan Sinha
Journal:  Perception       Date:  2003       Impact factor: 1.490

Review 2.  Fast robust automated brain extraction.

Authors:  Stephen M Smith
Journal:  Hum Brain Mapp       Date:  2002-11       Impact factor: 5.038

  2 in total
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1.  Automated Facial Recognition of Computed Tomography-Derived Facial Images: Patient Privacy Implications.

Authors:  Connie L Parks; Keith L Monson
Journal:  J Digit Imaging       Date:  2017-04       Impact factor: 4.056

2.  A Chinese multi-modal neuroimaging data release for increasing diversity of human brain mapping.

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Journal:  Sci Data       Date:  2022-06-09       Impact factor: 8.501

3.  Obscuring surface anatomy in volumetric imaging data.

Authors:  Mikhail Milchenko; Daniel Marcus
Journal:  Neuroinformatics       Date:  2013-01

4.  Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods.

Authors:  A de Sitter; M Visser; I Brouwer; K S Cover; R A van Schijndel; R S Eijgelaar; D M J Müller; S Ropele; L Kappos; Á Rovira; M Filippi; C Enzinger; J Frederiksen; O Ciccarelli; C R G Guttmann; M P Wattjes; M G Witte; P C de Witt Hamer; F Barkhof; H Vrenken
Journal:  Eur Radiol       Date:  2019-11-05       Impact factor: 5.315

5.  Multisite Comparison of MRI Defacing Software Across Multiple Cohorts.

Authors:  Athena E Theyers; Mojdeh Zamyadi; Mark O'Reilly; Robert Bartha; Sean Symons; Glenda M MacQueen; Stefanie Hassel; Jason P Lerch; Evdokia Anagnostou; Raymond W Lam; Benicio N Frey; Roumen Milev; Daniel J Müller; Sidney H Kennedy; Christopher J M Scott; Stephen C Strother; Stephen R Arnott
Journal:  Front Psychiatry       Date:  2021-02-24       Impact factor: 4.157

6.  De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

Authors:  Young-Hak Kim; Woo Hyun Shim; Yeon Uk Jeong; Soyoung Yoo
Journal:  J Med Internet Res       Date:  2020-12-10       Impact factor: 5.428

7.  A comparative study between state-of-the-art MRI deidentification and AnonyMI, a new method combining re-identification risk reduction and geometrical preservation.

Authors:  Ezequiel Mikulan; Simone Russo; Flavia Maria Zauli; Piergiorgio d'Orio; Sara Parmigiani; Jacopo Favaro; William Knight; Silvia Squarza; Pierluigi Perri; Francesco Cardinale; Pietro Avanzini; Andrea Pigorini
Journal:  Hum Brain Mapp       Date:  2021-09-14       Impact factor: 5.038

  7 in total

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