Segmentation of retinal blood vessels from ophthalmologic Diabetic Retinopathy images. (January 2019)

• Record Type: Journal Article
• Authors: Jebaseeli, T. Jemima; Durai, C. Anand Deva; Peter, J. Dinesh
• Is Part Of: Computers & electrical engineering. Volume 73(2019)
• Page Start: 245
• Page End: 258
• Publication Date: 2019-01
• Subjects: Diabetic Retinopathy -- Fundus image -- Retina -- Image segmentation -- Feature extraction -- Deep learning -- SVM -- Blood vessel -- Ophthalmology -- Neural network; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.11.024
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Contrast Limited Adaptive Histogram Equalization (CLAHE) eradicates false photographic artifacts and illuminations in fundus images. Tandem Pulse Coupled Neural Network (TPCNN) model operates on inter and intra channel linking of the input neurons and generates the automatic feature vectors. The cross channel linking enables the visibility of vessels at the cross over points. Classification and extraction of retinal blood vessels via Deep Learning Based Support Vector Machine (DLBSVM) helps the network to attain better data expression to improve the final classification result. Abstract: The most prominent ophthalmic cause of blindness is Diabetic Retinopathy (DR). This retinal disease is characterized by variation in diameter of the retinal blood vessel and the new blood vessel growth inside the retina. A system to enhance the quality of the segmentation result over the pathological retinal images has been proposed. The proposed method uses Contrast Limited Adaptive Histogram Equalization (CLAHE) for preprocessing and Tandem Pulse Coupled Neural Network (TPCNN) model for automatic feature vectors generation then classification and extraction of the retinal blood vessels via Deep Learning Based Support Vector Machine (DLBSVM). The proposed approach is assessed over the standard public fundus image databases to evaluate the performance. The results render that these techniques improve the segmentation results with an average value of 74.45% sensitivity, 99.40% specificity, and 99.16% accuracy. The results evoke that the proposed method is a suitable alternative for supervised techniques.