Houssem Ben Tahar

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Blood vessel detection from high resolution fundus images is a necessary step in several medical applications. Automatic blood vessels detection is a computing intensive task which raises the need for accelerated hardware architectures. In this paper, we propose a scalable hardware architecture for blood vessel detection using a matched filter (MF). The(More)
This paper presents a Zynq-based system to compute Run-Length encoding Matrix features for retinal image texture analysis. In order to improve the performance of the software implementation, we propose a co-processor architecture implemented in the programmable logic portion of the Zynq platform. Experimental results show a speedup of 26.3× with(More)
Segmenting the retinal vessels from fundus images is a prerequisite for many CAD systems for the automatic detection of diabetic retinopathy lesions. So far, research efforts have concentrated mainly on the accurate localization of the large to medium diameter vessels. However, failure to detect the smallest vessels at the segmentation step can lead to(More)
Blood vessel segmentation from high-resolution fundus images is a necessary step in several retinal pathologies detection. Automatic blood vessel segmentation is a computing-intensive task, which raises the need for acceleration with hardware architectures. In this paper, we propose two architectures for blood vessel segmentation using a matched filter(More)
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