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Automatic classification of facial morphology for medical applications

Marshall, Andrew David, Hicks, Yulia Alexandrovna and Abbas, Hawraa 2015. Automatic classification of facial morphology for medical applications. Procedia Computer Science 60 , pp. 1649-1658. 10.1016/j.procs.2015.08.275

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Abstract

Facial morphology measurement and classification play important role in the face anthropometry of many medical applications. This usually involves the investigation of medical abnormalities where specific facial features are studied by taking a number of measurements of the facial area under investigation. The measurements are often obtained from the three-dimensional (3D) scans of the faces; however, the measurements are often made manually, which is tedious and time consuming process. Moreover, in gene related studies thousands of measurements may be necessary in order to find statistically significant relationships between facial features and genes. Normative studies, from which typical populous models can be built, also require many measurements. Thus an automatic method to extract morphological measurements and interpret them is desirable. In this article, an automatic method for classification of facial morphology on the basis of a number of geometric measurements obtained automatically from 3D facial scans is presented. Among different facial features the philtrum, which is the vertical groove extending from the nose to the upper lip and the lip area, plays an important role in defining the interaction between the genes and craniofacial anomalies such as, for example, cleft lip and palate. In this paper, geometric features are analysed for their suitability to classify philtrum into three classes previously proposed by medical experts. Moreover, further analysis is conducted to assess the best number of classes to model the underlying data distribution from the point of view of classification accuracy. The obtained classification results are compared with the ground truth manual labelling of 3D face meshes provided by a medical expert. The dataset used for this research is taken from ALSPAC dataset and consists of 1000 3D face meshes. The proposed method achieves classification accuracy of 97% for this data set using the Mean, Minimum and Maximum curvature features in combination.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Elsevier
Date of First Compliant Deposit: 13 May 2016
Last Modified: 25 Aug 2018 21:40
URI: http://orca.cf.ac.uk/id/eprint/89738

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