Morphometric analysis of facial landmark data to characterize the facial phenotype associated with fetal alcohol syndrome

Mutsvangwa, Tinashe; Douglas, Tania S.

doi:10.1111/j.1469-7580.2006.00683.x

Cited by 39 publications

(53 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other recently reported human morphometric and 3D analysis data regarding abnormalities in the shape of the face have provided for the classification of children with FAS (e.g. Mutsvangwa & Douglas, 2007; Mutsvangwa et al, 2010). Facial morphometric techniques have also been applied to a mouse model to define strain-dependent ethanol-induced alterations with results indicating deficiencies in facial depth measures following GD7 exposure (Anthony et al, 2010), a finding that is consistent with the “flattened midface” associated with FAS (Moore et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance-Based Imaging in Animal Models of Fetal Alcohol Spectrum Disorder

et al. 2011

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) techniques, such as magnetic resonance microscopy (MRM), diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS), have recently been applied to the study of both normal and abnormal structure and neurochemistry in small animals. Herein, findings from studies in which these methods have been used for the examination of animal models of Fetal Alcohol Spectrum Disorder (FASD) are discussed. Emphasis is placed on results of imaging studies in fetal and postnatal mice that have highlighted the developmental stage dependency of prenatal ethanol exposure-induced CNS defects. Consideration is also given to the promise of methodological advances to allow in vivo studies of aberrant brain and behavior relationships in model animals and to the translational nature of this work.

show abstract

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance-Based Imaging in Animal Models of Fetal Alcohol Spectrum Disorder

et al. 2011

View full text Add to dashboard Cite

show abstract

“…She used genetic algorithm and eye template to automatically locate the eye landmarks in her study [7]. Mutsugawa and Douglas extensive study on the used of stereophotogammetry craniofacial anthropometry as a tool in FAS screening has been accepted as a standard procedure to screen FAS in children [8][9]. Automatic locating and extracting the facial landmarks proposed by Douglas is an alternative method to reduce the inter and intra examiner errors in craniofacial anthropometry.…”

Section: Introductionmentioning

confidence: 98%

Automatic Craniofacial Anthropometry Landmarks Detection and Measurements for the Orbital Region

Asi

Ismail

Ahmad

et al. 2014

Procedia Computer Science

View full text Add to dashboard Cite

Facial landmarks detection is undoubtedly important in many applications in computer vision for example the face detection and recognition. In craniofacial anthropometry, consistent landmarks localization as per standard definition of the craniofacial anthropometry landmarks is very important in order to get accurate craniofacial anthropometry data. In this article we demonstrated an automatic detection of craniofacial anthropometry landmarks at the orbital region. 3D images of 100 respondents' were photogaphed using Vectra-3D in controlled environment. Craniofacial measurements of 30 3D images were measured using VAM software. Two data sets of left and right eyes positive training data were created to train 'en' and 'ex' haar cascade classifiers. These classifiers were used to detect and locate the inner (en) and outer (ex) eye corners. We automatically measured the left and right eye fissures length (en-ex), the intercanthal (en-en) and the biocular (ex-ex) width. Statistical analysis was performed on the measurements taken by Vectra 3D and by our software with paired t-test and calculated the ICC indices. We observed quite amount of false positive detections. We removed the false positive and predicted the eye corners. Our classifiers able to detect and locate the 'en' and the 'ex' in 59 out of 60 test images. Our results show accurate detection of 'ex' and 'en' craniofacial landmarks as per standard definition. The paired t-test showed that all four (4) measurements are no significant difference with the p values on 95% confidence level are above 0.05. The ICC indices for the measurements were from 0.4 to 0.78. In conclusion, our trained enHaar and exHaar cascade classifiers were able to automatically detect the 'en' and 'ex' craniofacial anthropometry landmarks in controlled environment. The measurements were clinically no significant differences with the mean different were less than 1 mm in both eye fissures and intercanthal except the biocular width ( 1.16 mm). The consistency of the measurements between the two methods are good for the intercanthal width and moderate for the biocular width and for both eye fissure lengths.

show abstract

“…To define boundaries for these regions in face processing applications, most researchers have followed a point-based approach. Unfortunately, a variety of terminology is observed in the literature: a) Anchor point: Colbry and Stockman (2007), Colbry et al (2005) b) Keypoint: Mian et al (2008) c) Facial landmark: Gizatdinova and Surakka (2007), Mutsvangwa and Douglas (2007), Whitmarsh et al (2006), Gizatdinova and Surakka (2006) d) Fiducial point: Arca et al (2006), Wiskott et al (1997), Bronstein et al (2005) e) Feature point : Xiaoguang et al (2006), Hallinan et al (1999) This variety might be because in essence those names refer to different sets of points.…”

Section: Introductionmentioning

confidence: 99%

“…Other researchers, e.g. Mutsvangwa and Douglas (2007), used the term 'facial landmark' in their investigation. An extended list of terminology for a landmark is found in Dryden and Mardia (1999).…”

Section: Introductionmentioning

confidence: 99%

Landmark Localisation in 3D Face Data

Romero

Pears

2009

2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance

View full text Add to dashboard Cite

Accurate landmark localisation is an essential precursor to many 3D face processing algorithms but, as yet, there is a lack of convincing solutions that work well over a wide range of head poses.In this thesis, an investigation to localise facial landmarks from 3D images is presented, without using any assumption concerning facial pose. In particular, this research devises new surface descriptors, which are derived from either unstructured face data, or a radial basis function (RBF) model of the facial surface.A ground-truth of eleven facial landmarks is collected over well-registered facial images in the Face Recognition Grand Challenge (FRGC) database. Then, a range of feature descriptors of varying complexity are investigated to illustrate repeatability and accuracy when computed for the full set of eleven facial landmarks. At this stage, the nose-tip and two inner-eye corners are observed as the most distinctive facial landmarks as a tradeoff among repeatability, accuracy, and complexity. Thus, this investigation focuses on the localisation of these three facial landmarks, which is the minimum number of landmarks necessary for pose normalisation.Two new families of descriptors are introduced, namely point-pair and point-triplet descriptors, which require two and three vertices respectively for their computation. Also, two facial landmark localisation methods are investigated; in the first, a binary decision tree is used to implement a cascade filter, in the second, graph matching is implemented via relaxation by elimination. Then, using all of these descriptors and algorithms, a number of systems are designed to localise the nose-tip and two inner-eye corners. Above all, 99.92% of nose-tip landmarks within an accuracy of 12 mm is the best localisation performance, which is achieved by one cascade filter system. Finally, landmark localisation performance is reported by using a novel cumulative error curve. Localisation results are gathered by computing errors of estimated landmark locations against respective ground-truth data.2

show abstract

Morphometric analysis of facial landmark data to characterize the facial phenotype associated with fetal alcohol syndrome

Cited by 39 publications

References 38 publications

Magnetic Resonance-Based Imaging in Animal Models of Fetal Alcohol Spectrum Disorder

Magnetic Resonance-Based Imaging in Animal Models of Fetal Alcohol Spectrum Disorder

Automatic Craniofacial Anthropometry Landmarks Detection and Measurements for the Orbital Region

Landmark Localisation in 3D Face Data

Contact Info

Product

Resources

About