week1

week1

01.03.2023

Goals: background checks, task checks, organisational arrangements

1. Background

Optical coherence tomography (OCT) is a non-invasive and cross-sectional imaging technique that permits, for example, three-dimensional images with micrometer resolution to be obtained from within the retina. Since its invention in 1990, OCT has been adopted as an established quantitative imaging tool for the diagnosis and management of ocular diseases. To date, however, applications of OCT beyond biomedicine have been scarce as most commercially available OCT systems are specifically developed and optimized for medical applications. Recently, we and other groups have demonstrated that OCT can be used for industry applications including non-destructive evaluation of car paints and electronics circuits. 

2. Task

A typical OCT measurement will yield a 3D data cube with a size of 500x500x1024. In project, you will be provided a set of 3D images (600x550x1600) of spherical and/or aspherical lenses. your group will develop image processing algorithms to extract the surface position of the lenses from the 3D image data cube. The extracted lens surface position will be used to calculate the surface curvature of the lens which will then be compared to the actual surface curvature. 

3. organizational arrangement

Data analysis: Gaopeng Chen, Jun Die
Masking: Gaopeng Chen
Coordinates extraction  (first version): Gaopeng Chen, Yuchao Feng
Coordinates extraction (More accurate vesion): Gaopeng Chen
Sphere fitting: Gaopeng Chen
Local curvature: Jun Die
Quantifying the error: Jiajie Fan, Yuchao Feng

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week4

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Week4 22/03/2023 Goals for week4: l    Compare the extracted lens with fitting graph l    Using color map to show deviation l    Calculate the actual surface curvature According to fitting graph, we can get a spherical equat ion: Because we have got scatter plot of the extracted lens, the picture is below that: Then, substitute coordinates of all pairs of points into the spherical equation, which can get deviation of position. And according to the deviation, we can use  color map to show the difference clearly. In actuality, we have come up with many ways to calculate curvature, but some ways are not useful. For example, Gaussian curvature can be calculated by using a 3-D surface where there is equidistant grid variation of points. However, after fitting graph and choosing amounts of deviated points, we only get a set of scatter plot. As a result, we figured out another way to calculate the curvature of scatter plot: fitting a deterministic sphere by selecting   the proximity of a dete
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week3

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Week3 15/03/2023 Goals: Extraction coordinates Fitting the sphere 1. Extraction coordinates 1) First version: For the original 3D sphere, after slicing: the image pixels are first kept the same size as their pixel matrix and then converted to a heat map: After binarisation: Image after filtering and edge extraction : At this point we find that the extracted edge curves do not accurately represent the position of the lens surface because of the binarization process, and we used a different, more accurate method. 2)  More accurate version Masking : taking the four points on the lens through the luminance curve and substituting them to obtain the equation of the sphere, which is used as a mask to eliminate the image interference of platforms and stray points in the image data. Extraction of coordinates : Based on the masked 3d pixel matrix, get the exact 3d lens coordinate position by taking the point with the largest pixel value in the z-axis pixel curve along the plane formed by the x-a
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