EFFCA: Enhanced Frangi Filter for Coronary Angiography Segmentation on Mobile Edge Devices

To better identify the vessels, image segmentation techniques are often applied to coronary angiography (CAG) which reveals the functions and structures of heart's arteries using X-Ray images. Although deep learning based segmentation methods have shown their superiority in accuracy, they are often too complex for medical edge computing, a way to provides prompt diagnoses with minimum hardware cost. In this study, we investigate the method for CAG segmentation on mobile edge devices and propose a novel method, called Enhanced Frangi Filter for Coronary Angiography (EFFCA). Frangi filter is a classical method for vessel segmentation, but suffers from the problems of long processing time for multi-scale search and the vessel breakage problem. EFFCA utilizes a lightweight neural network to recognize the vessel patterns to decide the most suitable scales. It also employs the statistical and connectivity information of vessels to fix the vessel breakage from the segmented results. We have implemented EFFCA on mobile devices to demonstrate its usability. Experimental results show that EFFCA achieves a segmentation accuracy of 95.6% and a specificity of 96.2%, similar to the results of state-of-the-art models. Additionally, EFFCA offers the advantages of a much smaller code size, an efficient training process, and faster inference times on mobile edge devices.