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Retinal vasculature segmentation and measurement framework for color fundus and SLO images

Pachade Samiksha, Porwal Prasanna, Kokare Manesh, Giancardo Luca, Meriaudeau Fabrice

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

865--900

The change in vascular geometry is an indicator of various health issues linked with vision and cardiovascular risk factors. Early detection and diagnosis of these changes can help patients to select an appropriate treatment option when the disease is in its primary phase. Automatic segmentation and quantification of these vessels would decrease the cost and eliminate inconsistency related to manual grading. However, automatic detection of the vessels is challenging in the presence of retinal pathologies and non-uniform illumination, two common occurrences in clinical settings. This paper presents a novel framework to address the issue of retinal blood vessel detection and width measurement under these challenging circumstances and also on two different imaging modalities: color fundus imaging and Scanning Laser Ophthalmoscopy (SLO). In this framework, initially, vessel enhancement is done using linear recursive filtering. Then, a unique combination of morphological operations, background estimation, and iterative thresholding are applied to segment the blood vessels. Further, vessel diameter is estimated in two steps: firstly, vessel centerlines are extracted using the graph-based algorithm. Then, vessel edges are localized from the image profiles, by utilizing spline fitting to obtain vascular orientations and then finding the zero-crossings. Extensive experiments have been carried out on several publicly accessible datasets for vessel segmentation and diameter measurement, i.e., DRIVE, STARE, IOSTAR, RC-SLO and REVIEW dataset. Results demonstrate the competitive and comparable performance than earlier methods. The encouraging quantitative and visual performance of the proposed framework makes it an important component of a decision support system for retinal images.

An Electrooculography based Human Machine Interface for wheelchair control

Choudhari Ajit M., Porwal Prasanna, Jonnalagedda Venkatesh, Mériaudeau Fabrice

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 3

673--685

This paper presents a novel single channel Electrooculography (EOG) based efficient Human–Machine Interface (HMI) for helping the individuals suffering from severe paralysis or motor degenerative diseases to regain mobility. In this study, we propose a robust system that generates control command using only one type of asynchronous eye activity (voluntary eye blink) to navigate the wheelchair without a need of graphical user interface. This work demonstrates a simple but robust and effective multi-level threshold strategy to generate control commands from multiple features associated with the single, double and triple voluntary eye blinks to control predefined actions (forward, right turn, left turn and stop). Experimental trials were carried out on the able-bodied and disabled subjects to validate the universal applicability of the algorithms. It achieved an average command detection and execution accuracy of 93.89% with information transfer rate (ITR) of 62.64 (bits/ min) that shows the robust, sensitive and responsive features of the presented interface. In comparison with the established state of art similar HMI systems, our system achieved a better trade-off between higher accuracy and better ITR and while maintaining better performance in all qualitative and quantitative criteria. The results confirm that the proposed system offers a user-friendly, cost-effective and reliable alternative to the existing EOG-based HMI.