Visual parameter optimisation for biomedical image processing

Background: Biomedical image processing methods require users to optimise input parameters to ensure high quality output. This presents two challenges. First, it is difficult to optimise multiple input parameters for multiple input images. Second, it is difficult to achieve an understanding of underlying algorithms, in particular, relationships between input and output. Results: We present a visualisation method that transforms users’ ability to understand algorithm behaviour by integrating input and output, and by supporting exploration of their relationships. We discuss its application to a colour deconvolution technique for stained histology images and show how it enabled a domain expert to identify suitable parameter values for the deconvolution of two types of images, and metrics to quantify deconvolution performance. It also enabled a breakthrough in understanding by invalidating an underlying assumption about the algorithm. Conclusions: The visualisation method presented here provides analysis capability for multiple inputs and outputs in biomedical image processing that is not supported by previous analysis software. The analysis supported by our method is not feasible with conventional trial-and-error approaches

The use of computer-aided design techniques in dynamic graphical simulation

Imperial Users onl

A Study on the Performance Comparison of α-β-γ Filter and Kalman Filter for a Tracking Module on board High Dynamic Warships

Tracking refers to the estimation of the state of a target on motion with some degree of accuracy given at least one measurement. The measurement, which is the output obtained from sensors, contains system errors and errors resulting from the surrounding environment. Tracking filters play the key role of target state estimation after which the tracking system is updated. Therefore, the type of filter used in carrying out the estimations is crucial in determining the integrity and reliability of the updated value. This is especially true since different filters vary in their performance when subjected to different environments and initial conditions of motion dynamics. In addition, applications of different filter design methods have previously confirmed that filtering performance is a tradeoff between error reduction and a good transient response. Therefore, the criteria for selecting a particular filter for use in a tracking application depends on the given performance requirement. This study explores and investigates the operation of the Kalman filter and three α-β-γ tracking filter models that include Benedict-Bordner also known as the Simpson filter, Gray-Murray model and the fading memory α-β-γ filter. These filters are then compared based on the ability to reduce noise and follow a high dynamic target warship with minimum total lag error. The total lag error is the cumulative residual error computed from the difference between the true and the predicted positions, and the true and estimated positions for the given data samples. The results indicate that, although the Benedict-Bordner model performs poorly compared to the other filters in all aspects of performance comparison, the filter starts off sluggishly at the beginning of the tracking process as indicated by the overshooting on the trajectories, but stabilizes and picks up a good transient response as the tracking duration increases. The Gray-Murray model, on the other hand, demonstrates a better tracking ability as depicted by its higher accuracy and an even better response to a change in the target’s maneuver as compared to the Benedict-Bordner model. The Fading memory model out-performs the other two α-β-γ filters in terms of tracking and estimation error reduction, but based on sensitivity to target maneuvers and variance reduction ratio the Gray-Murray model demonstrates a slightly better performance. The Kalman filter, on the other hand, has a higher tracking accuracy compared to the α-β-γ filters which, however, have a higher sensitivity to target maneuvers and data stability as indicated by the steadier trajectories obtained. These results are a further proof that no one particular filter is perfect in all dimensions of selection criteria but it is rather a compromise that has to be made depending on the requirement of the physical system under consideration.Chapter 1 Introduction 1 1.1 Scope 1 1.2 Literature 2 1.2.1 Role of a Filter in a Physical System 2 1.2.2 Literature Review 3 1.3 Methodology and Contents 6 Chapter 2 Theory of Tracking Filters 8 2.1 Theory of α-β-γ Tracking Filter 8 2.1.1 Benedict-Bordner model 10 2.1.2 Gray-Murray model 10 2.1.3 The Fading memory model 11 2.2 Theory of the Kalman Filter 12 Chapter 3 Simulation 15 3.1 Initial Input of Target Dynamics 15 3.2 Input Motion Model of the Target Dynamics 15 3.3 Noise Modelling 16 3.4 α-β-γ Filter Weights Selection and Computation 17 3.4.1 Filter Gain Coefficient Selection Using Benedict-Bordner Model 17 3.4.2 Filter Gain Coefficient Selection Using Gray-Murray Model 18 3.4.3 Filter Gain Coefficient Selection Using Fading Memory Model 20 3.4.3.1 Fading memory model Optimization 22 3.4.3.1.1 Optimization by Position 23 3.4.3.1.2 Optimization by Velocity and Acceleration 28 3.5 Kalman Filter Tuning 31 3.5.1 Q Covariance Matrix Tuning 31 3.5.2 R Covariance Matrix Tuning 33 3.6 Result Analysis and Discussion 34 3.6.1 α-β-γ Filter Results and Remarks 34 3.6.2 Kalman Filter Results and Remarks 40 3.6.3 Kalman Filter vs. α-β-γ Filter 42 Chapter 4 Conclusion and Future Prospects 44 Reference 47 Acknowledgments 49Maste

On the integration of deformation and relief measurement using ESPI

The combination of relief and deformation measurement is investigated for improving the accuracy of Electronic Speckle-Pattern Interferometry (ESPI) data. The nature of sensitivity variations within different types of interferometers and with different shapes of objects is analysed, revealing significant variations for some common interferometers. Novel techniques are developed for real-time measurement of dynamic events by means of carrier fringes. This allows quantification of deformation and relief, where the latter is used in the correction of the sensitivity variations of the former

Computer-Generated Holography for Areal Additive Manufacture

With a market of approximately $10B, additive manufacture (AM) is an exciting next-generation technology with the promise of significant environmental and societal impact. AM promises to help reduce emissions and waste during manufacture while improving sustainability. Widely used in applications from hip implants to jet engines, AM remains the domain of experts due to the material and thermal challenges encountered. AM in metals is dominated by Laser Powder Based Fusion (L-PBF). Powder is spread in layers 10s of microns thick and selectively melted by scanning a small laser spot heat source over the bed. Traditional AM systems have limited ability to manage or compensate for heat generated. The rapidly moving heat source spot results in high thermal cycling and is a major influence on residual stress and distortion. Mechanical limitations in the galvoscanner mean that over or under-heating is common and can lead to voids, boiling and spatter. The scale difference between the part size and the spot size means that predictive modelling is beyond the scope of even today’s best computing clusters. These factors have led to frequent inability to ensure part quality without physical prototyping and destructive testing. This thesis sets out initial research into creating a radically new AM process that uses computer-generated holography (CGH) to produce complex light patterns in a single pulse. Projecting power to the whole layer at once will mean that the thermal properties of the powders before and after writing can be factored into the processed hologram and part design. It will also significantly reduce thermal gradients and melt-pool instability. The fields of additive manufacture and computer-generated holography are introduced in Chapter 1. Chapters 2 and 3 then provide more detail on CGH and AM modelling respectively. The first deliverable, a reusable software package capable of generating holograms, is presented in Chapter 4. Algorithms developed for the project are introduced in Chapter 4.3. The first project demonstrator, an AM machine capable of printing in resins using holographic projection is discussed in Section 6.2. This shows performance comparable to modern 3D printing machines and highlights the applicability of computer-generated holography to areal processes. Section 6.3 then discusses the ongoing development of a metal powder demonstrator. As this PhD forms the first stage of a larger project, only preliminary work on the powder demonstrator is discussed. Chapter 7 then draws conclusions and outlines the way forward for future research. The thesis appendices then discuss an in-depth discussion of algorithm performances in Appendices A and B. Appendices C and D then discuss digressions into the implementation. Appendices E and F present a laser induced damage threshold (LIDT) measurement system developed. Finally, Appendices G and H provide more detail on the software developed and Appendix I gives links to additional project resources.EP/T008369/1; EP/L016567/1; EP/V055003/