The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images
Open accessAbstract
Automated detection of blood vessel structures is becoming of crucial interest for better management of vascular disease. In this paper, we propose a new infinite active contour model that uses hybrid region information of the image to approach this problem. More specifically, an infinite perimeter regularizer, provided by using L(2) Lebesgue measure of the γ -neighborhood of boundaries, allows for better detection of small oscillatory (branching) structures than the traditional models based on the length of a feature's boundaries (i.e., H(1) Hausdorff measure). Moreover, for better general segmentation performance, the proposed model takes the advantage of using different types of region information, such as the combination of intensity information and local phase based enhancement map. The local phase based enhancement map is used for its superiority in preserving vessel edges while the given image intensity information will guarantee a correct feature's segmentation. We evaluate the performance of the proposed model by applying it to three public retinal image datasets (two datasets of color fundus photography and one fluorescein angiography dataset). The proposed model outperforms its competitors when compared with other widely used unsupervised and supervised methods. For example, the sensitivity (0.742), specificity (0.982) and accuracy (0.954) achieved on the DRIVE dataset are very close to those of the second observer's annotations.
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Cites
Chan, T. F., & Vese, L. A. (2001) · IEEE Transactions on Image Processing · doi:10.1109/83.902291 · Cited by 10,304
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Multiscale vessel enhancement filtering
Frangi, A. F., Niessen, W. J., Vincken, K. L., & Viergever, M. A. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0056195 · Cited by 3,707
Distance Regularized Level Set Evolution and Its Application to Image Segmentation
Li, C., Xu, C., Gui, C., & Fox, M. D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2069690 · Cited by 2,106
Minimization of Region-Scalable Fitting Energy for Image Segmentation
Li, C., Kao, C.-Y., Gore, J. C., & Ding, Z. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2008.2002304 · Cited by 1,721
Detection of blood vessels in retinal images using two-dimensional matched filters
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., & Goldbaum, M. (1989) · IEEE Transactions on Medical Imaging · doi:10.1109/42.34715 · Cited by 1,668
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
Soares, J. V. B., Leandro, J. J. G., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879967 · Cited by 1,497
Morphometric analysis of white matter lesions in MR images: method and validation
Zijdenbos, A. P., Dawant, B. M., Margolin, R. A., & Palmer, A. C. (1994) · IEEE Transactions on Medical Imaging · doi:10.1109/42.363096 · Cited by 1,265
Blood vessel segmentation methodologies in retinal images – A survey
Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.03.009 · Cited by 1,001
Marín, D., Aquino, A., Gegundez-Arias, M. E., & Bravo, J. M. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2064333 · Cited by 929
Cited by
A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation
Yan, Z., Yang, X., & Cheng, K.-T. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2872813 · Cited by 341
An active contour model based on local fitted images for image segmentation
Wang, L., Chang, Y., Wang, H., Wu, Z., Pu, J., & Yang, X. (2017) · Information Sciences · doi:10.1016/j.ins.2017.06.042 · Cited by 127
Augmented reality based real-time subcutaneous vein imaging system
Ai, D., Yang, J., Fan, J., Zhao, Y., Song, X., Shen, J., Shao, L., & Wang, Y. (2016) · Biomedical Optics Express · doi:10.1364/boe.7.002565 · Cited by 62
Self-citation
Metadata
- Authors
- Zhao, Y., Rada, L., Chen, K., Harding, S. P., & Zheng, Y.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2015-09
- Volume
- 34
- Issue
- 9
- Pages
- 1797-1807
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2037776979
- Cited by
- 391
- References
- 39
- FWCI
- 25.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zhao, Y., Rada, L., Chen, K., Harding, S. P., & Zheng, Y. (2015). Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2015.2409024
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