Theoretical and Experimental Evaluation of Spatial Resolution in a Variable Resolution X-Ray Computed Tomography Scanner

A variable resolution x-ray (VRX) computed tomography (CT) scanner can image objects of various sizes with greatly improved spatial resolution. The scanner employs an angulated discrete detector and achieves the resolution boost by matching the detector angulation to the scanner field of view (FOV) determined by the size of an object being imaged. A comprehensive evaluation of spatial resolution in an experimental version of the VRX CT scanner is presented in this dissertation. Two components of this resolution were evaluated – the pre-reconstruction spatial resolution, described by the detector presampling modulation transfer function (MTF), and the post-reconstruction spatial resolution, given by the scanner reconstruction MTF. The detector presampling MTF was modeled by the Monte Carlo simulation and measured by the moving-slit method. The modeled results showed the increase in the maximum cutoff frequency (in the detector plane) from 1.53 to 53.64 cycles per mm (cy/mm) as the scanner FOV decreased from 32 to 1 cm. The measured results supported the modeling, except for the small FOVs (below 8 cm), where the MTF could not be measured up to the cutoff frequency due to the focal-spot limitation. The scanner reconstruction MTF was measured by the special-phantom method. The measured results demonstrated the increase in the average cutoff frequency (in the object plane) from 2.44 to 4.13 cy/mm as the scanner FOV decreased from 16 to 8 cm. The MTF could not be measured at the FOVs other than 8 and 16 cm, due to the calibration-reconstruction inaccuracies and, again, the focal-spot limitation. Overall, the evaluation confirmed the potential value of the VRX CT scanner and produced results important for its further development

Image Processing Algorithms for Detection of Anomalies in Orthopedic Surgery Implants

Orthopedic implant procedures for hip implants are performed on 300,000 patients annually in the United States, with 22.3 million procedures worldwide. While most such operations are successfully performed to relieve pain and restore joint function for the duration of the patient\u27s life, advances in medicine have enabled patients to outlive the life of their implant, increasing the likelihood of implant failure. There is significant advantage to the patient, the surgeon, and the medical community in early detection of implant failures.The research work presented in this thesis demonstrates a non-invasive digital image processing technique for the automated detection of specific arthroplasty failures before requiring revision surgery. This thesis studies hip implant loosening as the primary cause of failure. A combination of digital image segmentation, representation and numerical description is employed and validated on 2-D X-ray images of hip implant phantoms to detect 3-D rotations of the implant, with the support of radial basis function neural networks to accomplish this task. A successful clinical implementation of the methods developed in this thesis can eliminate the need for revision surgery and prolong the life of the orthopedic implant

Over and beyond the Primate baubellum Surface: A “Jewel Bone” Shielded in Museums

Computed Tomography (CT), mostly used in the medical field, has also recently been involved in Cultural Heritage studies, thanks to its efficiency and total non-invasiveness. Due to the large variety of sizes and compositions typical of Cultural Heritage objects, different X-ray sources, detectors, and setups are necessary to meet the different needs of various case studies. Here, we focus on the use of micro-CT to explore the morphology and shape of a small, neglected bone found inside the clitoris of non-human primates (the baubellum), which we obtained by accessing two prestigious primatological collections of the American Museum of Natural History (New York, NY, USA) and the National Museum of Natural History (Washington, DC, USA). Overcoming methodological limits imposed by the absence of homologous landmarks, we combined the use of the non-invasive 3D micro-CT and a recently released landmark-free shape analysis (the alpha-shape technique) to objectively describe and quantify the shape complexity of scanned primate baubella. Micro-CT provided high-resolution results, overcoming constraints linked to museum policy about non-disruptive sampling and preserving samples for future research. Finally, it proved appropriate as post-mortem sampling had no impact on protected wild primate populations

Down syndrome detection using modified adaboost algorithm

In human body genetic codes are stored in the genes. All of our inherited traits are associated with these genes and are grouped as structures generally called chromosomes. In typical cases, each cell consists of 23 pairs of chromosomes, out of which each parent contributes half. But if a person has a partial or full copy of chromosome 21, the situation is called Down syndrome. It results in intellectual disability, reading impairment, developmental delay, and other medical abnormalities. There is no specific treatment for Down syndrome. Thus, early detection and screening of this disability are the best styles for down syndrome prevention. In this work, recognition of Down syndrome utilizes a set of facial expression images. Solid geometric descriptor is employed for extracting the facial features from the image set. An AdaBoost method is practiced to gather the required data sets and for the categorization. The extracted information is then assigned and used to instruct the Neural Network using Backpropagation algorithm. This work recorded that the presented model meets the requirement with 98.67% accuracy

Segmentation and Fracture Detection in CT Images for Traumatic Pelvic Injuries

In recent decades, more types and quantities of medical data have been collected due to advanced technology. A large number of significant and critical information is contained in these medical data. High efficient and automated computational methods are urgently needed to process and analyze all available medical data in order to provide the physicians with recommendations and predictions on diagnostic decisions and treatment planning. Traumatic pelvic injury is a severe yet common injury in the United States, often caused by motor vehicle accidents or fall. Information contained in the pelvic Computed Tomography (CT) images is very important for assessing the severity and prognosis of traumatic pelvic injuries. Each pelvic CT scan includes a large number of slices. Meanwhile, each slice contains a large quantity of data that may not be thoroughly and accurately analyzed via simple visual inspection with the desired accuracy and speed. Hence, a computer-assisted pelvic trauma decision-making system is needed to assist physicians in making accurate diagnostic decisions and determining treatment planning in a short period of time. Pelvic bone segmentation is a vital step in analyzing pelvic CT images and assisting physicians with diagnostic decisions in traumatic pelvic injuries. In this study, a new hierarchical segmentation algorithm is proposed to automatically extract multiplelevel bone structures using a combination of anatomical knowledge and computational techniques. First, morphological operations, image enhancement, and edge detection are performed for preliminary bone segmentation. The proposed algorithm then uses a template-based best shape matching method that provides an entirely automated segmentation process. This is followed by the proposed Registered Active Shape Model (RASM) algorithm that extracts pelvic bone tissues using more robust training models than the Standard ASM algorithm. In addition, a novel hierarchical initialization process for RASM is proposed in order to address the shortcoming of the Standard ASM, i.e. high sensitivity to initialization. Two suitable measures are defined to evaluate the segmentation results: Mean Distance and Mis-segmented Area to quantify the segmentation accuracy. Successful segmentation results indicate effectiveness and robustness of the proposed algorithm. Comparison of segmentation performance is also conducted using both the proposed method and the Snake method. A cross-validation process is designed to demonstrate the effectiveness of the training models. 3D pelvic bone models are built after pelvic bone structures are segmented from consecutive 2D CT slices. Automatic and accurate detection of the fractures from segmented bones in traumatic pelvic injuries can help physicians detect the severity of injuries in patients. The extraction of fracture features (such as presence and location of fractures) as well as fracture displacement measurement, are vital for assisting physicians in making faster and more accurate decisions. In this project, after bone segmentation, fracture detection is performed using a hierarchical algorithm based on wavelet transformation, adaptive windowing, boundary tracing and masking. Also, a quantitative measure of fracture severity based on pelvic CT scans is defined and explored. The results are promising, demonstrating that the proposed method not only capable of automatically detecting both major and minor fractures, but also has potentials to be used for clinical applications

Carpal Bone Analysis using Geometric and Deep Learning Models

The recent trend for analyzing 3D shapes in medical application has arisen new challenges for a vast amount of research activities. Quantitative shape comparison is a fundamental problem in computer vision, geometry processing and medical imaging. This thesis is motivated by the availability of carpal bone shape dataset to develop efficient techniques for diagnosis of a variety of wrist diseases and examine human skeletal. This study is conducted in two sections. First, we propose a spectral graph wavelet approach for shape analysis of carpal bones of the human wrist. More precisely, we employ spectral graph wavelets to represent the cortical surface of a carpal bone via the spectral geometric analysis of the Laplace-Beltrami operator in the discrete domain. We then propose global spectral graph wavelet (GSGW) descriptor that is isometric invariant, efficient to compute and combines the advantages of both low-pass and band-pass filters. Subsequently, we perform experiments on shapes of the carpal bones of ten women and ten men from a publicly-available database of wrist bones. Using one-way multivariate analysis of variance (MANOVA) and permutation testing, we show through extensive experiments that the proposed GSGW framework gives a much better performance compared to the global point signature (GPS) embedding approach for comparing shapes of the carpal bones across populations. In the second section, we evaluate bone age to assess children’s biological maturity and to diagnose any growth disorders in children. Manual bone age assessment (BAA) methods are timeconsuming and prone to observer variability by even expert radiologists. These drawbacks motivate us for proposing an accurate computerized BAA method based on human wrist bones X-ray images. We also investigate automated BAA methods using state-of-the-art deep learning models that estimate the bone age more accurate than the manual methods by eliminating human observation variations. The presented approaches provide faster assessment process and cost reduction in the hospitals/clinics. The accuracy of our experiments is evaluated using mean absolute error (MAE), and the results demonstrate that exploiting InceptionResNet-V2 model in our architecture achieves higher performance compared to the other used pre-trained models

DEVELOPMENT OF A PATIENT SPECIFIC IMAGE PLANNING SYSTEM FOR RADIATION THERAPY

A patient specific image planning system (IPS) was developed that can be used to assist in kV imaging technique selection during localization for radiotherapy. The IPS algorithm performs a divergent ray-trace through a three dimensional computed tomography (CT) data set. Energy-specific attenuation through each voxel of the CT data set is calculated and imaging detector response is integrated into the algorithm to determine the absolute values of pixel intensity and image contrast. Phantom testing demonstrated that image contrast resulting from under exposure, over exposure as well as a contrast plateau can be predicted by use of a prospective image planning algorithm. Phantom data suggest the potential for reducing imaging dose by selecting a high kVp without loss of image contrast. In the clinic, image acquisition parameters can be predicted using the IPS that reduce patient dose without loss of useful image contrast