Fibroatheroma identification in Intravascular Optical Coherence Tomography images using deep features.

Identifying vulnerable plaque is important in coronary heart disease diagnosis. Recent emerged imaging modality, Intravascular Optical Coherence Tomography (IVOCT), has been proved to be able to characterize the appearance of vulnerable plaques. Comparing with the manual method, automated fibroatheroma identification would be more efficient and objective.

Extract-and-match geometric corner and step pattern approach for registration of fluoroscopic X-ray sequences.

This paper presents a method to extract-and-match robust corner features based on connecting edges from the edge maps, mainly formed by coronary vascular junctions in fluoroscopic x-ray sequence images. Such images are challenging due to the aperture problem. To overcome this, existing approaches attempt to extract vessels for registration.

An automatic quantitative measurement method for performance assessment of retina image registration algorithms.

This paper presents a novel automatic quantitative measurement method for assessment of the performance of image registration algorithms designed for registering retina fundus images. To achieve automatic quantitative measurement, we propose the use of edges and edge dissimilarity measure for determining the performance of retina image registration algorithms. Our input is the registered pair of retina fundus images obtained using any of the existing retina image registration algorithms in the literature.

An augmented reality assistance platform for eye laser surgery.

This paper presents a novel augmented reality assistance platform for eye laser surgery. The aims of the proposed system are for the application of assisting eye doctors in pre-planning as well as providing guidance and protection during laser surgery. We developed algorithms to automatically register multi-modal images, detect macula and optic disc regions, and demarcate these as protected areas from laser surgery.

Geometric corner extraction in retinal fundus images.

This paper presents a novel approach of finding corner features between retinal fundus images. Such images are relatively textureless and comprising uneven shades which render state-of-the-art approaches e.g.