Statistical Shape Analysis for the Human Back

A thesis submitted to the department of Engineering and Technology in partial fulfilment of the requirements for the degree of Master of Philosophy in Production and Manufacturing Engineering at the University of WolverhamptonIn this research, Procrustes and Euclidean distance matrix analysis (EDMA) have been investigated for analysing the three-dimensional shape and form of the human back. Procrustes analysis is used to distinguish deformed backs from normal backs. EDMA is used to locate the changes occurring on the back surface due to spinal deformity (scoliosis, kyphosis and lordosis) for back deformity patients. A surface topography system, ISIS2 (Integrated Shape Imaging System 2), is available to measure the three-dimensional back surface. The system presents clinical parameters, which are based on distances and angles relative to certain anatomical landmarks on the back surface. Location, rotation and scale definitely influence these parameters. Although the anatomical landmarks are used in the present system to take some account of patient stance, it is still felt that variability in the clinical parameters is increased by the use of length and angle data. Patients also grow and so their back size, shape and form change between appointments with the doctor. Instead of distances and angles, geometric shape that is independent of location, rotation and scale effects could be measured. This research is mainly focusing on the geometric shape and form change in the back surface, thus removing the unwanted effects. Landmarks are used for describing back information and an analysis of the variability in positioning the landmarks has been carried out for repeated measurements. Generalized Procrustes analysis has been applied to all normal backs to calculate a mean Procrustes shape, which is named the standard normal shape (SNS). Each back (normal and deformed) is then translated, rotated and scaled to give a best fit with the SNS using ordinary Procrustes analysis. Riemannian distances are then estimated between the SNS and all individual backs. The highest Riemannian distance value between the normal backs and the SNS is lower than the lowest Riemannian distance value between the deformed backs and the SNS. The results shows that deformed backs can be differentiated from normal backs. EDMA has been used to estimate a mean form, variance-covariance matrix and mean form difference from all the normal backs. This mean form is named the standard normal form (SNF). The influence of individual landmarks for form difference between each deformed back and the SNF is estimated. A high value indicates high deformity on the location of that landmark and a low value close to 1 indicates less deformity. The result is displayed in a graph that provides information regarding the degree and location of the deformity. The novel aspects of this research lie in the development of an effective method for assessing the three-dimensional back shape; extracting automatic landmarks; visualizing back shape and back form differences

Semi-automatic Tracking of the Hyoid bone and the Epiglottis Movements in Digital Videofluoroscopic Images

Swallowing is a process that happens hundreds of times per day during eating, drinking, or swallowing saliva. Dysphagia is an abnormality in any stage of the swallowing process. It can cause serious problems such as dehydration and respiratory infection. In order to help dysphasic patients, radiologists need to evaluate the patient’s swallowing ability, usually using Video Fluoroscopic Swallowing Study (VFSS). During the assessment, several measurements are taken and evaluated, such as the displacement of the hyoid bone and epiglottis. Usually radiologists perform evaluation by means of visual inspection, which is a time consuming process that produces subjective results. Previous research has made strides automating swallowing measurements in order to produce objective results, but there is no study that automatically tracks the movement of the epiglottis. This thesis presents a design and implementation of a Computer Aided Diagnosis (CAD) system that can automatically track the movement of the hyoid bone and the epiglottis using minimal user input. The correlation between these two movements will be studied. With the aid of this system, radiologists can more reliably and efficiently take measurements and evaluate the health of the swallowing process

X-ray Image Segmentation and An Internet-based Tool for Medical Validation

Segmentation of vertebrae in X-ray images is a difficult task that requires an effective segmentation procedure. Noise, poor image contrast, occlusions and shape variability are some of the challenges in many of the spine X-ray images archived at the U.S. National Library of Medicine (NLM). In this thesis, we propose a curvature-based corner matching approach, which exploits the posterior corners of the vertebra to estimate the location and orientation of the vertebrae. The key advantage of the proposed approach is execution time, roughly about one-fifth of the previous approach that uses the generalized Hough transform when tested on a sizeable set of cervical spine images. This thesis also presents the first ever effort to develop a prototype internet-based medical image segmentation and pathology validation tool, which enables radiologists to validate computer generated image segmentations, modify existing or create new segmentation in addition to identifying pertinent pathology data

Quantifying Palaeopathology Using Geometric Morphometrics

Palaeopathology is the study of disease and injury in archaeological bone. Traditional methods rely heavily on macroscopic description which can have a high degree of subjectivity and error, as well as limiting the types of research questions possible. Geometric morphometrics are a suite of shape analysis techniques and provide an opportunity to investigate possible relationships between skeletal morphological variation and disease. This thesis aims to demonstrate the potential of applying these methods in palaeopathological research and the results illustrate the benefits of using quantifiable and objective shape analysis methods in palaeopathology. The first half of the thesis discusses the use of geometric morphometrics to investigate skeletal variation to identify possible aetiological factors in the development of Schmorl's nodes and osteoarthritis. There was a strong association found between vertebral morphology and Schmorl's nodes in the lower spine. These findings have great implications for both bioarchaeological interpretation and clinical understanding of the aetiology and pathogenesis of Schmorl's nodes. Joint morphology of the proximal ulna and distal humerus was found to have no identifiable relationship with osteoarthritis, indicating that joint morphology is not a predisposing factor in elbow osteoarthritis, nor does osteoarthritis deform the joints in a systematic manner. A tentative relationship between eburnation and knee joint morphology was identified, although these results need to be verified with future research. If the association can be supported, shape analyses may provide a way for clinicians to monitor the progression of the disease. Geometric morphometrics were also shown to objectively record pathological shape deformation resulting from leprosy and residual rickets. The ability to objectively describe lesions with quantified data will greatly strengthen palaeopathology by decreasing the subjectivity and error inherent in macroscopic based methods. This thesis represents promising groundwork for the incorporation of geometric morphometrics into palaeopathological research

Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.

Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd