Screening tests are an effective tool for the diagnosis and prevention of several diseases. Unfortunately, in order to produce an early diagnosis, the huge number of collected samples has to be processed faster than before. In particular this issue concerns image processing procedures, as they require a high computational complexity, which is not satisfied by modern software architectures. To this end, Field Programmable Gate Arrays (FPGAs) can be used to accelerate partially or entirely the computation. In this work, we demonstrate that the use of FPGAs is suitable for biomedical application, by proposing a case of study concerning the implementation of a vessels segmentation algorithm. The experimental results, computed on DRIVE and STARE databases, show remarkable improvements in terms of both execution time and power efficiency (6X and 5.7X respectively) compared to the software implementation. On the other hand, the proposed hardware approach outperforms literature works (3X speedup) without affecting the overall accuracy and sensitivity measures.

Software Implementation and Hardware Acceleration of Retinal Vessel Segmentation for Diabetic Retinopathy Screening Tests

CAVINATO, LARA;FIDONE, IRENE;BACIS, MARCO;Del Sozzo, E;Durelli, GC;Santambrogio, MD
2017

Abstract

Screening tests are an effective tool for the diagnosis and prevention of several diseases. Unfortunately, in order to produce an early diagnosis, the huge number of collected samples has to be processed faster than before. In particular this issue concerns image processing procedures, as they require a high computational complexity, which is not satisfied by modern software architectures. To this end, Field Programmable Gate Arrays (FPGAs) can be used to accelerate partially or entirely the computation. In this work, we demonstrate that the use of FPGAs is suitable for biomedical application, by proposing a case of study concerning the implementation of a vessels segmentation algorithm. The experimental results, computed on DRIVE and STARE databases, show remarkable improvements in terms of both execution time and power efficiency (6X and 5.7X respectively) compared to the software implementation. On the other hand, the proposed hardware approach outperforms literature works (3X speedup) without affecting the overall accuracy and sensitivity measures.
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1061027
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