Research of the Method for Assessing Facial Phenotypic Features from 2D Images in Medical Genetics

被引:0
|
作者
Kumov, V. S. [1 ]
Samorodov, A., V [1 ]
Kanivets, I., V [2 ]
Gorgisheli, K., V [2 ]
Solonichenko, V. G. [3 ]
机构
[1] Bauman Moscow State Tech Univ, Dept Biomed Engn, Moscow 105005, Russia
[2] Genomed Ltd, Moscow 115093, Russia
[3] Filatov Moscow Pediat Clin Hosp, Moscow 123001, Russia
关键词
Hereditary Diseases; Face Image; Facial Landmarks; Phenotypic Features;
D O I
10.5220/0010974700003123
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The paper proposes and investigates the phenotypic facial features recognition method based on facial points coordinates on a reconstructed 3D facial image. The accuracy of the determination of phenotypic features was investigated. The method recognizes phenotypic features with an accuracy of 84 % to 100 %. The method has been tested on open and own databases of facial images of patients with hereditary diseases. A criterion for the forming a risk group for Williams syndrome was proposed based on the summation of the absolute values of z-scores of phenotypic features. On own database, the criterion provides an AUC value of 0.922 for the separation of the Williams syndrome and the norm.
引用
收藏
页码:299 / 305
页数:7
相关论文
共 50 条
  • [1] 2D images calibration to facial features extraction
    Bravo, Daniel Trevisan
    Matiello Pellegrino, Sergio Roberto
    GRAPP 2007: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL GM/R, 2007, : 124 - 129
  • [2] Facial feature detection and face recognition from 2D and 3D images
    Wang, YJ
    Chua, CS
    Ho, YK
    PATTERN RECOGNITION LETTERS, 2002, 23 (10) : 1191 - 1202
  • [3] Facial expression analysis from 3D range images; Comparison with the analysis from 2D images and their integration
    Abui, TY
    Kenmochi, Y
    Kotani, K
    2003 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL 2, PROCEEDINGS, 2003, : 879 - 882
  • [4] Biomodels reconstruction based on 2D medical images
    Lopes, P.
    Flores, P.
    Seabra, E.
    COMPUTATIONAL MODELLING OF OBJECTS REPRESENTED IN IMAGES: FUNDAMENTALS, METHODS AND APPLICATIONS, 2007, : 361 - 365
  • [5] LEARNING 3D STRUCTURE FROM 2D IMAGES USING LBP FEATURES
    Herrera, Jose L.
    del-Blanco, Carlos R.
    Garcia, Narciso
    2014 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2014, : 2022 - 2025
  • [6] KNOWLEDGE-BASED 3D ANALYSIS FROM 2D MEDICAL IMAGES
    DHAWAN, AP
    ARATA, L
    IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, 1991, 10 (04): : 30 - 37
  • [7] 2D & 3D figural models of anatomic objects from medical images
    Pizer, SM
    Fritsch, DS
    Low, KC
    Furst, JD
    MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO IMAGE AND SIGNAL PROCESSING, 1998, 12 : 139 - 150
  • [8] 2D fuzzy adaptive threshold segmentation algorithm for facial images
    Xing, YJ
    Sun, J
    Pan, Y
    ISTM/2005: 6th International Symposium on Test and Measurement, Vols 1-9, Conference Proceedings, 2005, : 6505 - 6507
  • [9] Exploring Shape Deformation in 2D Images for Facial Expression Recognition
    Li, Jie
    Liu, Zhengxi
    Zhao, Qijun
    BIOMETRIC RECOGNITION (CCBR 2019), 2019, 11818 : 190 - 197
  • [10] Realistic relighting of 2D facial images using ratio optimization
    Yoon, Jong Won
    Jwa, Dong Hun
    Kim, Jae Hyup
    Park, Hyun
    Moon, Young Shik
    2007 INTERNATIONAL CONFERENCE ON INTELLIGENT PERVASIVE COMPUTING, PROCEEDINGS, 2007, : 228 - 231