Retinal blood vessel segmentation for macula detachment surgery monitoring instruments. (3rd May 2018)

• Record Type: Journal Article
• Authors: Hajabdollahi, Mohsen; Karimi, Nader; Reza Soroushmehr, S.M.; Samavi, Shadrokh; Najarian, Kayvan
• Is Part Of: International journal of circuit theory and applications. Volume 46:Number 6(2018)
• Page Start: 1166
• Page End: 1180
• Publication Date: 2018-05-03
• Subjects: artificial neural networks (ANN) -- error compensation -- hardware implementation -- retinal vessel segmentation -- retinopathy; Electric circuit analysis -- Periodicals; 621.319205
• DOI: 10.1002/cta.2462
• Languages: English
• ISSNs: 0098-9886

Abstract

Summary: In recent years image processing has improved detection and diagnosis in medical application. Image processing applications are now embedded in medical instruments such as MRI and CT. In the case of retinopathy, fast extraction of blood vessels can allow the physician to view injury regions during surgery. Macula detachment surgeries, or computer‐aided intraocular surgeries, require precise and real‐time knowledge of the vasculature during the operation. Use of artificial neural network has produced good results in image processing applications, but its implementation may not be suitable for real‐time applications in small, embedded hardware. Because of error resiliency of the neural network, its structure can be pruned and simplified. In this paper an efficient hardware implementation of neural network for retinal vessel segmentation is proposed. We simplify the neural network structure in such a way that the accuracy of the results is not altered significantly. Simulation results and FPGA implementation show that our proposed network has low complexity and can be applied for segmentation of retinal vessels with acceptable accuracy. This makes the proposed method a good candidate to be implemented in any device such as a binocular indirect ophthalmoscope. Abstract : Macula detachment surgeries, or computer‐aided intraocular surgeries, require precise and real‐time knowledge of the vasculature during the operation. In this paper an efficient hardware implementation of neural network for retinal vessel segmentation is proposed. We simplify the neural network structure in such a way that the accuracy of the results is not altered significantly. Simulation results and FPGA implementation show that our proposed network has low complexity and can be applied for segmentation of retinal vessels with acceptable accuracy.