Vision during manned booster operation Final report

Retinal images and accomodation control mechanism under conditions of space flight stres

The characteristics of the CAT to CAD to rapid prototyping system

ThesisComputer Aided Design (CAD), Rapid Prototyping (RP) and Computer Aided Tomography (CAT) technologies were researched. The project entails a unique combination of the abovementioned technologies, which had to be mastered by the author, on local and international terms. Nine software packages were evaluated to determine the modus operandi, required input and final output results. Fifty Rapid Prototyping systems were investigated to determine the strong and weak areas of the various systems, which showed that prototype materials, machine cost and growing time play an essential role. Thirty Reverse Engineering systems were also researched. Six different RE methods were recorded with several commercial systems available. Nineteen case studies were completed by using several different Computer Aided Tomography (CAT) and Magnetic Resonance Imaging (MRI) centers. Each scanning centre has different apparatus and is discussed in detail in the various case studies. The focus of this project is the data transfer of two dimensional CAT scanning data to threedimensional prototypes by using Reverse Engineering (RE) and Rapid Prototyping (RP). It is therefore of cardinal importance that one is familiar and understands the various fields of interest namely Reverse Engineering, Computer Aided Tomography and Rapid Prototyping. Each of these fields will be discussed in detail, with the latest developments in these fields covered as well. Case studies and research performed in the medical field should gain the medical industry's confidence. Constant marketing and publications will ensure that the technology is applied and transferred to the industry. Commercialisation of the technology is of utmost importanc

Imaging system performing substantially exact reconstruction and using non-traditional trajectories

A method and apparatus for reconstruction of a region of interest (ROI) for an object using an imaging system is provided. The imaging system may substantially exactly reconstruct the ROI with a straight line trajectory. In the straight line trajectory, the ROI is not bounded or encircled by the actual trajectory of the source (e.g., no chords that are composed from two points on the source trajectory intersect or fill the ROI to be imaged). However, the ROI may be substantially reconstructed by using "virtual" chords to reconstruct the ROI. The virtual chords are such that no point on the trajectory is included in the virtual chord (such as one that is parallel to the straight line trajectory). These virtual chords may intersect and fill the ROI, thus enabling substantially exact reconstruction. Further, in reconstructing the image, the straight line trajectory may be assumed to be infinite in length

Modeling picking on pharmaceutical tablets

Tablets are the most popular solid dosage form in the pharmaceutical industry because they are cheap to manufacture, chemically and mechanically stable and easy to transport and fairly easy to control dosage. Pharmaceutical tableting operations have been around for decades however the process is still not well understood. One of the common problems faced during the production of pharmaceutical tablets by powder compaction is sticking of powder to the punch face, This is known as \u27sticking\u27. A more specialized case of sticking is picking when the powder is pulled away form the compact in the vicinity of debossed features. In the pharmaceutical industry, picking is solved by trial and error which is an expensive, labor intensive and time consuming affair. The objective of this work was to develop, validate, and implement a modeling framework for predicting picking in powder compacts. The model was developed in Abaqus a commercially available finite element package. The resulting model was used to investigate the influence of debossed feature geometry viz. the stroke angle and degree of pre-pick, and, influence of lubricant on picking. (Abstract shortened by ProQuest.

Inter-comparison of medical image segmentation algorithms

Segmentation of images is a vital part of medical image processing, and MRI (Magnetic Resonance Imaging) is already recognized as a very important tool for clinical diagnosis. In this thesis, comparisons between different segmentation algorithms are carried out, specifically on brain MRI images. Initial parts of the thesis provide the background to the project, and an introduction to the basic principles of MRI, respectively, followed by parameter definitions and MRI image artifacts. The next part briefly covers various image pre-processing techniques which are required, and this is followed with a review of the major segmentation techniques which are available, including thresholding, region growing, clustering, and K-Means clustering. The concept of fuzzy logic is also introduced here, and the chapter concludes with a discussion of fuzzy logic based segmentation algorithms such as Fuzzy C-Means (FCM) and Improved Fuzzy C-Means (IFCM) clustering algorithms. The following part provides details concerning the source, type and parameters of the data (images) used for this thesis. Evaluation and inter-comparisons between a number of different segmentation algorithms are given in near concluding part, finally, conclusions and suggestions for future research are provided in last part. Qualitative comparisons on real images and quantitative comparisons on simulated images were performed. Both qualitative and quantitative comparisons demonstrated that fuzzy logic based segmentation algorithms are superior in comparison with classical segmentation algorithms. Edge-based segmentation algorithms demonstrated the poorest performance of all; K-means and IFCM clustering algorithms performed better, and FCM demonstrated the best performance of all. However, it should be noted that IFCM was not properly evaluated due to time restrictions in code generation, testing and evaluation.Segmentation of images is a vital part of medical image processing, and MRI (Magnetic Resonance Imaging) is already recognized as a very important tool for clinical diagnosis. In this thesis, comparisons between different segmentation algorithms are carried out, specifically on brain MRI images. Initial parts of the thesis provide the background to the project, and an introduction to the basic principles of MRI, respectively, followed by parameter definitions and MRI image artifacts. The next part briefly covers various image pre-processing techniques which are required, and this is followed with a review of the major segmentation techniques which are available, including thresholding, region growing, clustering, and K-Means clustering. The concept of fuzzy logic is also introduced here, and the chapter concludes with a discussion of fuzzy logic based segmentation algorithms such as Fuzzy C-Means (FCM) and Improved Fuzzy C-Means (IFCM) clustering algorithms. The following part provides details concerning the source, type and parameters of the data (images) used for this thesis. Evaluation and inter-comparisons between a number of different segmentation algorithms are given in near concluding part, finally, conclusions and suggestions for future research are provided in last part. Qualitative comparisons on real images and quantitative comparisons on simulated images were performed. Both qualitative and quantitative comparisons demonstrated that fuzzy logic based segmentation algorithms are superior in comparison with classical segmentation algorithms. Edge-based segmentation algorithms demonstrated the poorest performance of all; K-means and IFCM clustering algorithms performed better, and FCM demonstrated the best performance of all. However, it should be noted that IFCM was not properly evaluated due to time restrictions in code generation, testing and evaluation