In this section, a various levelled picture matting method is utilized to extract veins from fundus pictures. All the more explicitly, a various levelled methodology is joined into the picture matting strategy for vein apportioning. For the most part, the matting strategy requires a client indicated tri map, which isolates the info picture into three districts: the frontal area, foundation, and obscure areas. Be that as it may, producing a client indicated tri map is very tuff work for vessel parcelling undertakings. In this task, we propose a technique that creates tri map consequently by using area highlights of veins. At that point, we apply a various levelled picture tangling strategy to separate the vessel components in the obscure districts. The suggested technique has less count time and performs better than numerous other regulated and solo strategies. The datasets CHASE_DB1, STARE and DRIVE are adopted as these datasets are available openly. By applying these three datasets on pictures produces fixed time of 50.71, 15.74 and 10.72 s with optimized parcelling exactness 93.9%, 94.6% and 95.1%, respectively.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Alaa A et al (2017) Pair-activity analysis from video using qualitative trajectory calculus. IEEE Tran Cir Sys Vid Tec 28(8):1850–1863
Almotiri J et al (2018) Retinal vessels segmentation techniques and algorithms; a survey. Appl Sci 8(2):155
Amal C et al. (2021) Retinal blood vessel segmentation in fundus images based on morphological operators within entropy information. In: 13th international conference on machine vision, international society for optics and photonics, vol. 11605, p. 116050Z
Arjina M et al (2021) A novel solution of using mixed reality in bowel and oral and maxillofacial surgical telepresence: 3D mean value cloning algorithm. Int J Med Rob Com Assi Sur 17(4):2224
Cho, D et al., (2016) Natural image matting using deep convolutional neural networks. In: European conference on computer vision, pp. 626–643. Springer: Cham
Debao T, Zhang Z, (2020) Double discriminative graph regularized semantic auto-encoder for zero-shot learning. In: 2021 2nd international conference artificial intelligence and information systems, pp. 1–7
Fan Z et al (2018) A hierarchical image matting model for blood vessel segmentation in fundus images. IEEE Trans Image Process 28(5):2367–2377
Ferreira C.A. et al., (2019) quantitative assessment of central serous chorioretinopathy in angiographic sequences of retinal images. In: 2019 IEEE 6th Portuguese meeting on bioengineering (ENBENG), pp. 1–4
Grant H et al (2020) Deep learning in medical image registration: a survey. Mac vis App 31(1):1–18
Hong X et al. (2018) Improving comprehensive sampling sets matting using texture feature. In: 2018 IEEE 4th international conference computer communication (ICCC), pp. 1617–1621
Jiachang L et al (2021) Cross-modality person re-identification via channel-based partition network. Appl Intell. https://doi.org/10.1007/s10489-021-02548-3
Joao VBS, (2015) Efficient image segmentation and segment-based analysis in computer vision applications. PhD Dissertation
Kupersmith MJ, Sibony PA (2021) Retinal and optic nerve deformations due to orbital versus intracranial venous hypertension. J Neuro Ophthomol 41(3):321–328
Liming L et al (2021) Retinal vessel segmentation based on W-net conditional generative adversarial nets. J Med Imaging Health Inf 11(7):2016–2024
Lin W et al., (2021) Medical matting: a new perspective on medical segmentation with uncertainty. arXiv preprint
Ludovic S et al., (2021) Cerebellar connectivity maps embody individual adaptive behavior. bioRxiv
Maheswari MV, and Murugeswari G, (2021) Exploration of image processing techniques and datasets for retina image analysis. In: 2021 third international conference international communication technologies and virtual mobile networks (ICICV), pp. 943–949. IEEE
Manzoor R et al., (2019) Multi-scale feature fused single shot detector for small object detection in UAV images. In: international conference on computer vision systems, Springer: Cham, pp. 778–786
Musa A, (2021) Medical image segmentation using deep neural networks. In: 2021 29th signal processing and communications application conference (SIU), pp. 1–4. IEEE
Nandy RS, (2021) Segmentation of retinal blood vessel structure based on statistical distribution of the area of isolated objects. In: Recent Trends in Computational Intelligence Enabled Research, Academic Press, pp. 263–278
Paolo R et al., (2018) Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas. In: 2018 international conference on computing and network communication (CoCoNet), pp. 50–57
Perez M et al (2018) P139 Automatic classification of arterial and venular trees in colour fundus images. Artery Res 24:119
Raju KHP et al (2021) An efficient detection of micro aneurysms from fundus images. Mat Today Proc. https://doi.org/10.1016/j.matpr.2020.12.867
Sangeethaa SN, Maheswari PU (2018) An intelligent model for blood vessel segmentation in diagnosing DR using CNN. J Med Sys 42(10):1–10
Santiago C et al (2019) Histological data of axons, astrocytes, and myelin in deep subcortical white matter populations. Data Brief 23:103762
Shuzhi S et al (2021) An orthogonal locality and Globality dimensionality reduction method based on Twin Eigen Decomposition. IEEE Access 9:55714–55725
Sridhar GR, Lakshmi G, (2021) Artificial intelligence in medicine: diabetes as a model. In: artificial intelligence for information management: a healthcare perspective, Springer: Singapore, pp. 283–305
Su YJ, Chuang YY (2015) Disambiguating stereoscopic transparency using a Thaumatrope approach. IEEE Trans vis Comput Grap 21(8):959–969
Tang Y et al (2020) ResWnet for retinal small vessel segmentation. IEEE Access 8:198265–198274
Udayini D et al., (2021) A comprehensive analysis of morphological process dependent retinal blood vessel segmentation. In: 2021 international conference on computing, communication and intelligent systems (ICCCIS), pp. 510–516. IEEE
Xue C et al (2021) Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review. Quant Imaging Med Surg 11(10):4431–4460
Yahya A, Boufama B (2021) Biomedical image segmentation: a survey. SN Com Sci 2(4):1–22
Yi R et al (2020) Double graph regularized double dictionary learning for image classification. IEEE Trans Image Process 29:7707–7721
Zhang Y et al. (2019) A late fusion cnn for digital matting. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 7469–7478
Zhenlin H et al., (2021) 3D inversion of potential field data based on fully convolutional networks. In: 1st international meeting for applied geoscience & energy, society of exploration geophysicists, pp. 951–955
Zihe H et al (2021) Automatic retinal vessel segmentation based on an improved U-Net approach. Sci Prog. https://doi.org/10.1155/2021/5520407
Zubair KM, Lee Y, (2021) Screening fundus images to extract multiple ocular features: a unified modeling approach. In: 2021 IEEE embs international conference in biomedical and health informatics (BHI), pp. 1–5. IEEE
The authors have no relevant financial or non-financial interests to disclose.
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Swathi, S., Sushma, S., Devi Supraja, C. et al. A hierarchical image matting model for blood vessel segmentation in retinal images. Int J Syst Assur Eng Manag (2021). https://doi.org/10.1007/s13198-021-01397-0
- Hierarchical strategy
- Picture matting method
- Region properties
- Trimap generation
- Vessel partitioning