14 May 2018 Computationally efficient blood vessels segmentation in fundus image on shared memory parallel machines
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Blood vessels segmentation in fundus image is a requiring step in order to detect retinopathies. A higher performing segmentation was been proposed in [12]. It consists at three dependent stages: Provide two binary images to extract wide vessels, compute features of the remaining pixels on binary images in order to extract fine vessels, and then combine both wide and fine vessels. The segmentation execution time is about 3-12 seconds when it is performed with fundus image having resolutions between 768*584 and 999*960. These latest resolutions are quite smaller than ones provided by actual retinograph, which leads to a higher rise on execution time. In this paper, we propose a parallelism strategy of the segmentation approach for implementation in Shared Memory Parallel Machine (SMPM). First, both binary images are provided in parallel. Thereafter, features processing is split according to their computational complexities. At the later stage, wide vessels and fine vessels images are subdivided adequately in the objective of a parallel combination. The parallel strategy is implemented using OpenCV and then assessed on STARE public data sets. Experimental analyses of execution time and efficiency are presented and discussed.
Conference Presentation
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Yaroub Elloumi, Yaroub Elloumi, Mohamed Akil, Mohamed Akil, } "Computationally efficient blood vessels segmentation in fundus image on shared memory parallel machines", Proc. SPIE 10670, Real-Time Image and Video Processing 2018, 1067008 (14 May 2018); doi: 10.1117/12.2304740; https://doi.org/10.1117/12.2304740

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