Super-image mosaic of infant retinal fundus: Selection and registration of the best-quality frames from videos

Abstract

Wide-field retinal fundus cameras are commercially available devices that allow acquiring videos of a wide area of infants' eye, considered of clinical interest in screening for ROP (Retinopathy of Prematurity). Many frames of the video are often altered by defects such as artifacts, interlacing and defocus, which make critical and time consuming the search and choice of the good frames to be analyzed. We developed a computerized system that automatically selects the best still frames from the video and builds a mosaic from these images. It will allow clinicians to examine a single large, best quality image. The best frames are identified using several image quality parameters that measure sharpness and steadiness, and then registered to obtain a single mosaic image. A custom blending procedure is then applied in order to provide a final image with homogeneous luminosity and contrast, devoid of the dark areas typically present in the outer regions of single frames. The best-frame selection module showed a PPV of 0.92, while the visual inspection of resulting mosaics confirmed the remarkable capability of the proposed system to provide higher quality images.

Automated frame selection process for high-resolution microendoscopy

“…7 In high-resolution microendoscopy a coherent fiber optic bundle is placed in contact with the tissue, providing a realtime video display showing the size, shape, and distribution of cell nuclei in the epithelium. 20 These algorithms are generally designed to remove redundant video frames and/or identify key frames in order to spare clinicians the need to spend time viewing and selecting individual images, thus enhancing diagnostic efficiency. The images may be subjectively interpreted by trained personnel or they may be quantitatively analyzed using image analysis algorithms to diagnostically assess relevant parameters, such as N/C ratio and nuclear size.…”

Automated image analysis of corneal structures in anterior-segment optical coherence tomography and in-vivo confocal microscopy images

“…In addition, we use the properties of the graph to determine the optimal order in which to register the images. The method follows by using a distance weighting scheme shown in previous works to work for retinal and corneal imagery [25,91] to seamlessly blend the images together. The algorithm continues iteratively using the resulting panoramas from the initial image-pair results to determine if any more connections exist between the panoramas created in the first pass.…”

“…Poletti et al proposed a similar algorithm based on the same distance functions [79,91] except they considered a form where the distance functions could be raised to a power, d I 1 n (i, j). In their works, the distance functions were squared while our work simply kept them as is.…”