Bryan M. Williams

Measurement of the Intertablet Coating Uniformity of a Pharmaceutical Pan Coating Process With Combined Terahertz and Optical Coherence Tomography In-Line Sensing

We present in-line coating thickness measurements acquired simultaneously using 2 independent sensing modalities: terahertz pulsed imaging (TPI) and optical coherence tomography (OCT). Both techniques are sufficiently fast to resolve the coating thickness of individual pharmaceutical tablets in situ during the film coating operation, and both techniques are direct structural imaging techniques that do not require multivariate calibration. The TPI sensor is suitable to measure coatings greater than 50 μm and can penetrate through thick coatings even in the presence of pigments over a wide range of excipients. Due to the long wavelength, terahertz radiation is not affected by scattering from dust within the coater. In contrast, OCT can resolve coating layers as thin as 20 μm and is capable of measuring the intratablet coating uniformity and the intertablet coating thickness distribution within the coating pan. However, the OCT technique is less robust when it comes to the compatibility with excipients, dust, and potentially the maximum coating thickness that can be resolved. Using a custom-built laboratory scale coating unit, the coating thickness measurements were acquired independently by the TPI and OCT sensors throughout a film coating operation. Results of the in-line TPI and OCT measurements were compared against one another and validated with off-line TPI and weight gain measurements. Compared with other process analytical technology sensors, such as near-infrared and Raman spectroscopy, the TPI and OCT sensors can resolve the intertablet thickness distribution based on sampling a significant fraction of the tablet populations in the process. By combining 2 complementary sensing modalities, it was possible to seamlessly monitor the coating process over the range of film thickness from 20 μm to greater than 250 μm.

Multiscale sequential convolutional neural networks for simultaneous detection of fovea and optic disc

Abstract Detecting the locations of the optic disc and fovea is a crucial task towards developing automatic diagnosis and screening tools for retinal disease. We propose to address this challenging problem by investigating the potential of applying deep learning techniques to this field. In the proposed method, simultaneous detection of the centers of the fovea and the optic disc (OD) from color fundus images is considered as a regression problem. A deep multiscale sequential convolutional neural network (CNN) is designed and trained. The publically available MESSIDOR and Kaggle datasets are used to train the network and evaluate its performance. The centers of the fovea and the OD in each image were marked by expert graders as the ground truth. The proposed method achieves an accuracy of 97%, 96.7% for the detection of the OD center and 96.6%, 95.6% for the detection of the foveal center of the MESSIDOR and Kaggle test sets respectively. Our promising results demonstrate the excellent performance of the proposed CNNs in simultaneously detecting the centers of both the fovea and OD without human intervention or handcrafted features. Moreover, we can localize the landmarks of an image in 0.007s. This approach could be used as a crucial part of automated diagnosis systems for better management of eye disease.

High resolution corneal and single pulse imaging with line field spectral domain optical coherence tomography

We report the development of a Spectral Domain Line Field Optical Coherence Tomography (LF-OCT) system, using a broad bandwidth and spatial coherent Super-Continuum (SC) source. With conventional quasi-Continuous Wave (CW) setup we achieve axial resolutions up to 2.1 μm in air and 3D volume imaging speeds up to 213 kA-Scan/s. Furthermore, we report the use of a single SC pulse, of 2 ns duration, to temporally gate an OCT B-Scan image of 70 A-Scans. This is the equivalent of 35 GA-Scans/s. We apply the CW setup for high resolution imaging of the fine structures of a human cornea sample ex-vivo. The single pulse setup is applied to imaging of a coated pharmaceutical tablet. The fixed pattern noise due to spectral noise is removed by subtracting the median magnitude A-Scan. We also demonstrate that the Fourier phase can be used to remove aberration caused artefacts.

A new constrained total variational deblurring model and its fast algorithm

Although image intensities are non-negative quantities, imposing positivity is not always considered in restoration models due to a lack of simple and robust methods of imposing the constraint. This paper proposes a suitable exponential type transform and applies it to the commonly-used total variation model to achieve implicitly constrained solution (positivity at its lower bound and a prescribed intensity value at the upper bound). Further to establish convergence, a convex model is proposed through a relaxation of the transformed functional. Numerical algorithms are presented to solve the resulting non-linear partial differential equations. Test results show that the proposed method is competitive when compared with existing methods in simple cases and more superior in other cases.

Non-destructive analysis of flake properties in automotive paints with full-field optical coherence tomography and 3D segmentation

Automotive coating systems are designed to protect vehicle bodies from corrosion and enhance their aesthetic value. The number, size and orientation of small metallic flakes in the base coat of the paint has a significant effect on the appearance of automotive bodies. It is important for quality assurance (QA) to be able to measure the properties of these small flakes, which are approximately 10μm in radius, yet current QA techniques are limited to measuring layer thickness. We design and develop a time-domain (TD) full-field (FF) optical coherence tomography (OCT) system to scan automotive panels volumetrically, non-destructively and without contact. We develop and integrate a segmentation method to automatically distinguish flakes and allow measurement of their properties. We test our integrated system on nine sections of five panels and demonstrate that this integrated approach can characterise small flakes in automotive coating systems in 3D, calculating the number, size and orientation accurately and consistently. This has the potential to significantly impact QA testing in the automotive industry.

A New Study of Blind Deconvolution with Implicit Incorporation of Nonnegativity Constraints

The inverse problem of image restoration to remove noise and blur in an observed image was extensively studied in the last two decades. For the case of a known blurring kernel (or a known blurring type such as out of focus or Gaussian blur), many effective models and efficient solvers exist. However when the underlying blur is unknown, there have been fewer developments for modelling the so-called blind deblurring since the early works of You and Kaveh (1996) and Chan and Wong (1998). A major challenge is how to impose the extra constraints to ensure quality of restoration. This paper proposes a new transform based method to impose the positivity constraints automatically and then two numerical solution algorithms. Test results demonstrate the effectiveness and robustness of the proposed method in restoring blurred images.

Automatic Detection and Distinction of Retinal Vessel Bifurcations and Crossings in Colour Fundus Photography

The analysis of retinal blood vessels present in fundus images, and the addressing of problems such as blood clot location, is important to undertake accurate and appropriate treatment of the vessels. Such tasks are hampered by the challenge of accurately tracing back problems along vessels to their source. This is due to the unresolved issue of distinguishing automatically between vessel bifurcations and vessel crossings in colour fundus photographs. In this paper, we present a new technique for addressing this problem using a convolutional neural network approach to firstly locate vessel bifurcations and crossings and then to classifying them as either bifurcations or crossings. Our method achieves high accuracies for junction detection and classification on the DRIVE dataset and we show further validation on an unseen dataset from which no data has been used for training. Combined with work in automated segmentation, this method has the potential to facilitate: reconstruction of vessel topography, classification of veins and arteries and automated localisation of blood clots and other disease symptoms leading to improved management of eye disease.

An effective variational model for simultaneous reconstruction and segmentation of blurred images

The segmentation of blurred images is of great importance. There have been several recent pieces of work to tackle this problem and to link the areas of image segmentation and image deconvolution in the case where the blur function κ is known or of known type, such as Gaussian, but not in the case where the blur function is not known due to a lack of robust blind deconvolution methods. Here we propose two variational models for simultaneous reconstruction and segmentation of blurred images with spatially invariant blur, without assuming a known blur or a known blur type. Based on our recent work in blind deconvolution, we present two solution methods for the segmentation of blurred images based on implicitly constrained image reconstruction and convex segmentation. The first method is aimed at obtaining a good quality segmentation while the other is aimed at improving the speed while retaining the quality. Our results demonstrate that, while existing models are capable of segmenting images corrupted by small amounts of blur, they begin to struggle when faced with heavy blur degradation or noise, due to the limitation of edge detectors or a lack of strict constraints. We demonstrate that our new algorithms are effective for segmenting blurred images without prior knowledge of the blur function, in the presence of noise and offer improved results for images corrupted by strong blur.

Imaging of Corneal Neovascularization: Optical Coherence Tomography Angiography and Fluorescence Angiography

Purpose The purpose of this study was to compare optical coherence tomography angiography (OCTA) and indocyanine green angiography (ICGA) for the assessment of corneal neovascularization (CoNV). Methods Patients with CoNV extending at least 3 mm into the cornea were included. All patients underwent corneal imaging at the same visit. Images were recorded using the AngioVue OCTA system (Optovue, Inc.) with the long corneal adaptor module (CAM-L). ICGA images were recorded with fluorescent filters using the Heidelberg system (HRA2 Scanning Laser Ophthalmoscope; Heidelberg Engineering). Images were graded for quality by two independent observers. Vessel parameters: area, number, diameter, branch and end points, and tortuosity, were compared between devices. Bland-Altman plots were used to assess differences between parameters. Results Fifteen patients with CoNV predominantly associated with microbial keratitis were included. Mean subjective image quality score was better for ICGA (3.3 ± 0.9) than for OCTA (2.1 ± 1.2, P = 0.002), with almost perfect interobserver agreement for ICGA images (κ = 0.83) and substantial agreement for OCTA images (κ = 0.69). Agreement of grading of all investigated vessel parameters between ICGA and OCT images was slight to moderate, with significant differences found for vessel diameter (-8.98 μm, P = 0.01, 95% limits of agreement [LOA]: -15.89 to -2.07), number of branch (25.93, P = 0.09, 95% LOA: -4.31 to 56.17), and terminal points (49, P = 0.05, 95% LOA: 0.78 to 97.22). Conclusion Compared with ICGA, current OCTA systems are less precise in capturing small vessels in CoNV complexes, and validation studies are needed for OCTA segmentation software. OCTA, however, complements ICGA by providing evidence of red blood cell flow, which together with depth information, may be helpful when planning treatment of CoNV.

A new image deconvolution method with fractional regularisation

Image deconvolution is an important pre-processing step in image analysis which may be combined with denoising, also an important image restoration technique, and prepares the image to facilitate diagnosis in the case of medical images and further processing such as segmentation and registration. Considering the variational approach to this problem, regularisation is a vital component for reconstructing meaningful information and the problem of defining appropriate regularisation is an active research area. An important question in image deconvolution is how to obtain a restored image which has sharp edges where required but also allows smooth regions. Many of the existing regularisation methods allow for one or the other but struggle to obtain good results with both. Consequently, there has been much work in the area of variational image reconstruction in finding regularisation techniques which can provide good quality restoration for images which have both smooth regions and sharp edges. In this paper, we propose a new regularisation technique for image reconstruction in the blind and non-blind deconvolution problems where the precise cause of blur may or may not be known. We present experimental results which demonstrate that this method of regularisation is beneficial for restoring images and blur functions which contain both jumps in intensity and smooth regions.