Retinal Blood Vessel Segmentation Using Deep Learning

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Description

Abstract:

               The condition of the vascular network of the human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to the variable size of vessels, relatively low contrast, and the potential presence of pathologies like microaneurysms and hemorrhages. The Project proposes Retinal image analysis through efficient detection of vessels and exudates for retinal vasculature disorder analysis. It plays important role in the detection of some diseases in the early stages, such as diabetes, which can be performed by comparing the states of retinal blood vessels. Intrinsic characteristics of retinal images make the blood vessel detection process difficult. Here, we proposed a new algorithm to detect the retinal blood vessels effectively. The green channel will be selected for image analysis to extract vessels accurately. The Daubechies wavelet transform is used to enhance the image contrast for effective vessels detection. The directionality feature of the multi-structure elements method makes it an effective tool in edge detection. Hence, morphology operators using multi-structure elements are applied to the enhanced image in order to find the retinal image ridges. Afterward, morphological operators by reconstruction eliminate the ridges not belonging to the vessel tree while trying to preserve the thin vessels unchanged. In order to increase the efficiency of the morphological operators by reconstruction, they were applied using multi-structure elements. A simple thresholding method along opening and closing indicates the remained ridges belonging to vessels. The experimental result proves that the blood vessels and exudates can be effectively detected by applying this method to the retinal images.


System Analysis

   Existing Systems

  • Edge detection methods
  • Segmentation like simple and global thresholding algorithm
  • SVM

Proposed System:

  • The combination of multi-structure morphological process 
  •  Segmentation technique
  • Feature extraction
  • Neural Network

Advantages:

  • Accurate retina vessel and execute detection
  • It is useful in diabetic diagnosis

Block Diagram

Retinal Blood Vessel Segmentation Using Deep Learning
Retinal Blood Vessel Segmentation Using Deep Learning

Requirement Specifications

Hardware Requirements

  • system
  • 4 GB of RAM
  • 500 GB of Hard disk

SOFTWARE REQUIREMENTS:

  • MATLAB 2018b

REFERENCES

[1] M. E. Martinez-Perez, A. D. Hughes, S. A. Thom, and K. H. Parker, “Improvement of a retinal blood vessel segmentation method using the insight segmentation and registration toolkit (ITK),” in Proc. IEEE 29th Annu. Int. Conf. EMBS. Lyon, IA, France, 2007, pp. 892–895.

[2] S. Dua, N. Kandiraju, and H.W. Thompson, “Design and implementation of a unique blood-vessel detection algorithm towards early diagnosis of glaucomatous retinopathy,” in Proc. IEEE Int. Conf. in Inf. Technol., Coding Comput., 2005, pp. 26–31.

[3] A.M.Mendonc¸a and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.

[4] A. Aquino, M. E. Gegúndez-Arias, and D. Marín, “Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques,” IEEE Trans. Med. Imag., vol. 29, no. 11, pp. 1860–1869, Nov. 2010.

[5] M. Lalonde, M. Beaulieu, and L. Gagnon, “Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching,” IEEE Trans. Med. Imag., vol. 20, no. 11, pp. 1193–1200, Nov. 2001

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