Extended Focused Image in White Light Scanning Interference Microscopy

Altamar-Mercado, Hernando; Patiño-Vanegas, Alberto; Marrugo, Andrés G.

doi:10.1364/isa.2019.ith1c.3

Cited by 1 publication

(2 citation statements)

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“…To find the location of artifacts, we use templates that resemble their typical shape. Among the properties of these artifacts, we find that dust particles are made of a single material, and their projections are approximately monochromatic [12]. Dust artifacts are darker in the center, and their opacity is attenuated far from the center, and most of them are approximately circular in shape [9].…”

Section: Artifact Localization Via Cross-correlation Non-maxima Suppression and Spatial Matchingmentioning

confidence: 99%

“…However, this detection approach requires careful scanning through many possible focus positions. Also, the removal stage is based on a fusion approach from the stack of images typically used in microscopy [12]. Alternatively, Zhou and Lin [13] propose the detection of dust candidate regions via an optimization approach to estimate a dust-free image.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of transverse mode instability in Yb, Tm and Ho doped bent high-power fiber amplifiers

Vazeerpour¹

2021

Opt. Pura Apl.

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Dust particle artifacts are present in all imaging modalities but have more adverse consequences in medical images like retinal images. They could be mistaken as small lesions, such as microaneurysms. We propose a method for detecting and accurately segmenting dust artifacts in retinal images based on multi-scale template-matching on several input images and an iterative segmentation via an inpainting approach. The inpainting is done through dictionary learning and sparse-based representation. The artifact segmentation is refined by comparing the original image to the initial restoration. On average, 90% of the dust artifacts were detected in the test images, with state-of-theart restoration results. All detected artifacts were accurately segmented and removed. Even the most challenging artifacts located on top of blood vessels were removed. Thus, ensuring the continuity of the retinal structures. The proposed method successfully detects and removes dust artifacts in retinal images, which could be used to avoid false-positive lesion detections or as an image quality criterion. An implementation of the proposed algorithm can be accessed and executed through a Code Ocean compute capsule.

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Section: Artifact Localization Via Cross-correlation Non-maxima Suppression and Spatial Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%