Retinal blood vessel segmentation using pixel-based feature vector

Toptas B., HANBAY D.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL, cilt.70, 2021 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 70
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.bspc.2021.103053
  • Dergi Adı: BIOMEDICAL SIGNAL PROCESSING AND CONTROL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, INSPEC
  • Anahtar Kelimeler: Biomedical imaging, Retinal blood vessel segmentation, Image segmentation, Feature extraction, FUNDUS IMAGES, OPTIC DISC, MATHEMATICAL MORPHOLOGY, AUTOMATED DETECTION, FILTERS, WAVELET, FOVEA
  • İnönü Üniversitesi Adresli: Evet

Özet

A lot of important disease information can be accessed by performing retinal blood vessel analysis on fundus images. Diabetic retinopathy is one of the diseases understood by retinal blood vessel analysis. If this disease is detected at an early stage, vision loss can be prevented. In this paper, a method that performs retinal blood vessel analysis with classical methods is proposed. In this proposed system, pixel-based feature extraction is performed. Five different feature groups are used for feature extraction. These feature groups are edge detection, morphological, statistical, gradient, and Hessian matrix. An 18-D feature vector is created for each pixel. This feature vector is given to the artificial neural network for training. Using test images, the system is tested on two publicly available datasets. Sensitivity, Specificity, and Accuracy performance measures were used as success measures. The similarity index between the segmented image and the ground truth is measure using Dice and Jaccard. The accuracy of the system was measured as 96.18% for DRIVE and 94.56% for STARE, respectively. Experimental results show that the proposed algorithm achieves satisfactory results. This method can be used as an automated retinal blood vessel segmenting system.