Abstract
This paper presents a new method for reliably detecting retinal vessel tree using a local flow phase stretch transform (LF-PST). A local flow evaluator is proposed to increase the local contrast and the coherence of the local orientation of vessel tree. This is achieved by incorporating information about the local structure and direction of vessels, which is estimated by introducing a second curvature moment evaluation matrix (SCMEM). The SCMEM evaluates vessel patterns as only features having linearly coherent curvature. We present an oriented phase stretch transform to capture retinal vessels running at various diameters and directions. The proposed method exploits the phase angle of the transform, which includes structural features of lines and curved patterns. The LF-PST produces several phase maps, in which the vessel structure is characterized along various directions. To produce an orientation invariant response, all phases are linearly combined. The proposed method is tested on the publicly available DRIVE and IOSTAR databases with different imaging modalities and achieves encouraging segmentation results outperforming the state-of-the-art benchmark methods.
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Challoob, M., Gao, Y. (2020). A Local Flow Phase Stretch Transform for Robust Retinal Vessel Detection. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_22
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