Down Syndrome Diagnosis Based on Gabor Wavelet Transform

SARAYDEMİR S., TAŞPINAR N., EROĞUL O., Kayserili H., Dinckan N.

JOURNAL OF MEDICAL SYSTEMS, cilt.36, sa.5, ss.3205-3213, 2012 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36 Sayı: 5
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1007/s10916-011-9811-1
  • Dergi Adı: JOURNAL OF MEDICAL SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.3205-3213
  • Anahtar Kelimeler: Down syndrome, Gabor wavelet transform, Face recognition, Classification, Dysmorphology, FACE RECOGNITION, TEXTURE SEGMENTATION, FACIAL MORPHOLOGY, DYSMORPHIC FACES, FILTERS, EIGENFACES, NETWORKS
  • Erciyes Üniversitesi Adresli: Evet

Özet

Down syndrome is a chromosomal condition caused by the presence of all or part of an extra 21st chromosome. It has different facial symptoms. These symptoms contain distinctive information for face recognition. In this study, a novel method is developed to distinguish Down Syndrome in a custom face database. Gabor Wavelet Transform (GWT) is used as a feature extraction method. Dimension reduction is performed with Principal Component Analysis (PCA). New dimension which has most valuable information is derived with Linear Discriminant Analysis (LDA). Classification process is implemented with k-nearest neighbor (kNN) and Support Vector Machine (SVM) methods. The classification accuracy is carried out 96% and 97,34% with kNN and SVM methods, respectively. Different from the studies related with the Down Sydrome, feature selection process is applied before PCA according to the correlation between components of feature vectors. Best results are achieved with euclidean distance metric for kNN and linear kernel type for SVM. In this way, we developed an efficient system to recognize Down syndrome.