In the production of display screen modules, multi-faceted quality control is performed. One of the processes is detection of defects on and between module components such as particles, scratches and air bubbles using a 3D optical microscope. Technicians view a stack of images of potential defect areas and make a qualitative assessment of the sample. However, this is made difficult by the artifacts in the unfocused image layers. Moreover, there is a large discrepancy in the detection tendencies of the technicians. In order to standardize and automate the classification of major and minor defects in products, we propose a convolutional neural network based binary classification that makes use of the normal angle and oblique angle images. The decision factors affecting the classification of the sample include defect position, size, and shape. In order to reflect these factors, the microscopic images of the sample are taken in varying focal depths from normal and oblique angles. Then, the maximum intensity projection (MaxIP) and minimum intensity projection (MinIP) in the xy, yz, xz plane are created. The set of MaxIP and MinIP are used to train a modified VGG-network. Each plane differs in size, so MaxIP and MinIP of every plane was independently added as input to the network and were concatenated in the fully connected layer. Being that the dataset used for this work composed of 185 major defect samples and 2036 minor defect samples, augmentation was essential. In order to even out the major and minor defect sample ratio, random affine transformation was performed on the major defect sample images. The proposed method of binary classification performs with a total accuracy of 98.6%.
Low-dose CT has been investigated and employed in various forms for clinical practices. One of the viable options for low-dose imaging is using a multi-slit beam collimator to achieve a sparse sampling. We have earlier demonstrated the feasibility of such technique, and extended the method for dual-energy imaging from a single scan using a multi-slit beam-filter in the circular CBCT system. Multi-detector-row helical CT is indeed in wide use in the clinics. In this work, we continue to explore the beam-filter based imaging technique in the multi-row helical CT scans. We conducted a simulation study by applying a virtual filter to the real data acquired from a helical CT scanner. We separated the sinograms of each row from the multi-row helical CT data, and removed the streaks using a notch filter in the Fourier domain of each sinogram. We then reconstructed the image by use of the filtered-backprojection algorithm and reduced the image noise by applying l0-norm based smoothing.
In this study, we aim to separate the ghost artifacts from the limited angle CT image by using Robust Principle Component Analysis (RPCA) and thus improve the reconstructed CT images. Conventionally, RPCA method separates the foreground and the background. Often, the background is assumed as static or quasi-static. When applied to limited angle CT images, the artifacts are considered as quasi-static background whereas the anatomical structures are considered foreground. Thus, RPCA is performed to segment the foreground from the background. Finally, different post-reconstruction de-noising parameters are applied to each foreground and background to remove the artifact effectively.
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