Analysis of photogrammetrically-derived digital surface and terrain models for building recognition

Bibliography: leaves 79-83.Buildings are one of the most frequently occurring man-made objects and in urban scenes their detection and reconstruction, e.g., in the form of three-dimensional CAD (computer aided design) models, is very important to many users such as architects, town planners and telecommunications and environmental engineers. This thesis examines the role of digital terrain and surface models in supporting this reconstruction process. The thesis is structured into four main parts, namely, image matching to derive the data sets, building detection to delineate buildings from other man-made objects in DSM (digital surface model), DSM quality analysis to determine the reliability of the data, hydrological analysis to determine flood zones as an additional example of DTM application and finally conclusions and possible future outlook. Image matching was performed using an in-house image matching software in the Geomatics department. Off-the-shelf GIS functionality was used to tackle building detection, DSM quality analysis and hydrological analysis. A key feature of GIS functionality is the ability to exploit standard functions for the input/output, management, spatial analysis, editing and visualisation. It also aims at enhancing the accessibility of developed tools to end users

Integrated Graph Theoretic, Radiomics, and Deep Learning Framework for Personalized Clinical Diagnosis, Prognosis, and Treatment Response Assessment of Body Tumors

Purpose: A new paradigm is beginning to emerge in radiology with the advent of increased computational capabilities and algorithms. The future of radiological reading rooms is heading towards a unique collaboration between computer scientists and radiologists. The goal of computational radiology is to probe the underlying tissue using advanced algorithms and imaging parameters and produce a personalized diagnosis that can be correlated to pathology. This thesis presents a complete computational radiology framework (I GRAD) for personalized clinical diagnosis, prognosis and treatment planning using an integration of graph theory, radiomics, and deep learning. Methods: There are three major components of the I GRAD framework–image segmentation, feature extraction, and clinical decision support. Image Segmentation: I developed the multiparametric deep learning (MPDL) tissue signature model for segmentation of normal and abnormal tissue from multiparametric (mp) radiological images. The segmentation MPDL network was constructed from stacked sparse autoencoders (SSAE) with five hidden layers. The MPDL network parameters were optimized using k-fold cross-validation. In addition, the MPDL segmentation network was tested on an independent dataset. Feature Extraction: I developed the radiomic feature mapping (RFM) and contribution scattergram (CSg) methods for characterization of spatial and inter-parametric relationships in multiparametric imaging datasets. The radiomic feature maps were created by filtering radiological images with first and second order statistical texture filters followed by the development of standardized features for radiological correlation to biology and clinical decision support. The contribution scattergram was constructed to visualize and understand the inter-parametric relationships of the breast MRI as a complex network. This multiparametric imaging complex network was modeled using manifold learning and evaluated using graph theoretic analysis. Feature Integration: The different clinical and radiological features extracted from multiparametric radiological images and clinical records were integrated using a hybrid multiview manifold learning technique termed the Informatics Radiomics Integration System (IRIS). IRIS uses hierarchical clustering in combination with manifold learning to visualize the high-dimensional patient space on a two-dimensional heatmap. The heatmap highlights the similarity and dissimilarity between different patients and variables. Results: All the algorithms and techniques presented in this dissertation were developed and validated using breast cancer as a model for diagnosis and prognosis using multiparametric breast magnetic resonance imaging (MRI). The deep learning MPDL method demonstrated excellent dice similarity of 0.87±0.05 and 0.84±0.07 for segmentation of lesions on malignant and benign breast patients, respectively. Furthermore, each of the methods, MPDL, RFM, and CSg demonstrated excellent results for breast cancer diagnosis with area under the receiver (AUC) operating characteristic (ROC) curve of 0.85, 0.91, and 0.87, respectively. Furthermore, IRIS classified patients with low risk of breast cancer recurrence from patients with medium and high risk with an AUC of 0.93 compared to OncotypeDX, a 21 gene assay for breast cancer recurrence. Conclusion: By integrating advanced computer science methods into the radiological setting, the I-GRAD framework presented in this thesis can be used to model radiological imaging data in combination with clinical and histopathological data and produce new tools for personalized diagnosis, prognosis or treatment planning by physicians

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification

The use of satellite remote sensing to determine the spatial and temporal distribution of surface water on the eastern shores of Lake St. Lucia.

Thesis (M.Sc.)-University of KwaZulu-Natal, Durban, 2006.The Eastern Shores of Lake St Lucia forms part of the ecologically important Greater St Lucia Wetland Park, designated a World Heritage Site in 1999. The landscape is characterised by surface water, a high water table and numerous wetlands. Little is known about the distribution and temporal fluctuations of this surface water and its relationship to the wetlands. This study uses remote sensing to examine the relationship by mapping the extent of seasonal, ephemeral and permanent surface water on the Eastern Shores. Much of the surface water occurs in conjunction with emergent vegetation and is not easily mapped using hard classification methods. Neither a cluster analysis nor a maximum likelihood classification were able to map the subtle variations of the water-vegetation mix. Much more successful was the application of spectral mixture analysis using image endmembers of water, woody vegetation and non-woody vegetation. This technique was applied to seven Landsat Thematic Mapper images from 1991, 2001 and 2002. Steep slopes, forests and bare sand were masked out prior to classification. Maps of water extent were produced for each of the seven study dates. Mapping accuracy was verified against rainfall, with high correlations being obtained against rainfall accumulated over six months and longer. Long-term rainfall patterns were reflected in the surface water distribution, with inundation being more extensive when accumulated rainfall was high. Fire scars reduced the accuracy of the spectral mixture analysis but these scars could be identified from the thermal image bands. The largest open water body in the study area was Lake Bhangazi. Large extents of surface water were also found in the Mfabeni swamp and the wilderness area to the north where water concentrations of 90% were measured during wet periods. Surface water present near Brodies Crossing during wet periods was less evident when rainfall was lower. No inundation was recorded in the areas to the west and south-west of the Mfabeni swamp or in the southern parts of the study area. The techniques used in this study were developed into a water mapping protocol that uses image endmembers and spectral mixture analysis to measure water concentration

Vessel identification in diabetic retinopathy

Diabetic retinopathy is the single largest cause of sight loss and blindness in 18 to 65 year olds. Screening programs for the estimated one to six per- cent of the diabetic population have been demonstrated to be cost and sight saving, howeverthere are insufficient screening resources. Automatic screen-ing systems may help solve this resource short fall. This thesis reports on research into an aspect of automatic grading of diabetic retinopathy; namely the identification of the retinal blood vessels in fundus photographs. It de-velops two vessels segmentation strategies and assess their accuracies. A literature review of retinal vascular segmentation found few results, and indicated a need for further development. The two methods for vessel segmentation were investigated in this thesis are based on mathematical morphology and neural networks. Both methodologies are verified on independently labeled data from two institutions and results are presented that characterisethe trade off betweenthe ability to identify vesseland non-vessels data. These results are based on thirty five images with their retinal vessels labeled. Of these images over half had significant pathology and or image acquisition artifacts. The morphological segmentation used ten images from one dataset for development. The remaining images of this dataset and the entire set of 20 images from the seconddataset were then used to prospectively verify generaliastion. For the neural approach, the imageswere pooled and 26 randomly chosenimageswere usedin training whilst 9 were reserved for prospective validation. Assuming equal importance, or cost, for vessel and non-vessel classifications, the following results were obtained; using mathematical morphology 84% correct classification of vascular and non-vascular pixels was obtained in the first dataset. This increased to 89% correct for the second dataset. Using the pooled data the neural approach achieved 88% correct identification accuracy. The spread of accuracies observed varied. It was highest in the small initial dataset with 16 and 10 percent standard deviation in vascular and non-vascular cases respectively. The lowest variability was observed in the neural classification, with a standard deviation of 5% for both accuracies. The less tangible outcomes of the research raises the issueof the selection and subsequent distribution of the patterns for neural network training. Unfortunately this indication would require further labeling of precisely those cases that were felt to be the most difficult. I.e. the small vessels and border conditions between pathology and the retina. The more concrete, evidence based conclusions,characterise both the neural and the morphological methods over a range of operating points. Many of these operating points are comparable to the few results presented in the literature. The advantage of the author's approach lies in the neural method's consistent as well as accurate vascular classification.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Morphometry of human lung with physiological correlations

Resected lobes from patients having pre-operative pulmonary function tests were fixed by inflation with formal saline and cut into 1cm parasagittal slices. Randomly selected tissue, from the lateral two slices, was plastic embedded and sections prepared for microscopic analysis.A semi-automatic image analysis system was used to quantitate bronchiolar calibre and shape and peribronchiolar attachment number, inter-alveolar attachment distance and the amount of macroscopic emphysema. An automatic image analyser (IBAS2) was used to measure alveolar surface area per-unit volume (AWUV).Measured bronchiolar calibre (minimum diameter and measured lumen area) was not related to patient height, lung volume, pulmonary function or other morphometric variables.AWUV, mean inter-alveolar attachment distance, theoretical lumen area and bronchiolar shape were independent of patient size and lung volume, but were inter-related. A combination of low AWUV and loss of attachments profoundly affected bronchiolar shape. However, AWUV and alveolar attachment loss were not always in proportion and demonstrated different functional effects: AWUV affects carbon monoxide transfer factor whereas attachments affect the slope of phase III and forced expiratory volume with bronchiolar shape affecting closing volume.Macroscopic emphysema did not accurately reflect the extent of alveolar wall loss as identified by AWUV and showed poor correlations with pulmonary function tests.Computerised axial tomography (CT scan) exhibited a strong correlation with AWUV and can be used to assess lung density in life

Part-based Grouping and Recognition: A Model-Guided Approach

Institute of Perception, Action and BehaviourThe recovery of generic solid parts is a fundamental step towards the realization of general-purpose vision systems. This thesis investigates issues in grouping, segmentation and recognition of parts from two-dimensional edge images. A new paradigm of part-based grouping of features is introduced that bridges the classical grouping and model-based approaches with the purpose of directly recovering parts from real images, and part-like models are used that both yield low theoretical complexity and reliably recover part-plausible groups of features. The part-like models used are statistical point distribution models, whose training set is built using random deformable superellipse. The computational approach that is proposed to perform model-guided part-based grouping consists of four distinct stages. In the first stage, codons, contour portions of similar curvature, are extracted from the raw edge image. They are considered to be indivisible image features because they have the desirable property of belonging either to single parts or joints. In the second stage, small seed groups (currently pairs, but further extension are proposed) of codons are found that give enough structural information for part hypotheses to be created. The third stage consists in initialising and pre-shaping the models to all the seed groups and then performing a full fitting to a large neighbourhood of the pre-shaped model. The concept of pre-shaping to a few significant features is a relatively new concept in deformable model fitting that has helped to dramatically increase robustness. The initialisations of the part models to the seed groups is performed by the first direct least-square ellipse fitting algorithm, which has been jointly discovered during this research; a full theoretical proof of the method is provided. The last stage pertains to the global filtering of all the hypotheses generated by the previous stages according to the Minimum Description Length criterion: the small number of grouping hypotheses that survive this filtering stage are the most economical representation of the image in terms of the part-like models. The filtering is performed by the maximisation of a boolean quadratic function by a genetic algorithm, which has resulted in the best trade-off between speed and robustness. Finally, images of parts can have a pronounced 3D structure, with ends or sides clearly visible. In order to recover this important information, the part-based grouping method is extended by employing parametrically deformable aspects models which, starting from the initial position provided by the previous stages, are fitted to the raw image by simulated annealing. These models are inspired by deformable superquadrics but are built by geometric construction, which render them two order of magnitudes faster to generate than in previous works. A large number of experiments is provided that validate the approach and, since several new issues have been opened by it, some future work is proposed

Change and variation in a hyer-arid cultural landscape: A merhodological approach using remote sensing timeseries (Landsat MSS and TM, 1973-1996) from the Wadi vegetation of the eastern desert of Egypt

Nine wadi localities in a hyper-arid environment have been registered in the field and studied using earth observation data. Branch height, crown – and trunk – diameter, and indicators of land-use such as present traces of browsing, lopping and charcoal production were registered for arboreal vegetation, mostly Acacia tortilis and Balanites aegyptiaca. A point mapping (GPS) was selected to optimise subsequent integration with raster data and to facilitate a detailed interpretation of change images. Field data and change images are interpreted according to two gradients, one cultural and one hydrological. Derived tree maps are overlaid referenced TM data in order to detect differences between pixels with and without vegetation. The Red band is the most consistent spectral band in its content of vegetation information. Nevertheless it is apparent that several methodological and technical factors constrain the possibilities to register vegetation in this environment of very scarce vegetation cover. Similar problems are also recognised in the change analysis which is based on the difference between Red bands of the years compared. Four different datasets are part of the analysis: 1973, 1979, 1984 (all Landsat MSS images) and 1996 (TM). Field data indicate that changes are taking place in the cultural landscape of the Eastern Desert, and the change is primarily due to processes that both in causes and consequences is associated with ‘deforestation’. Although several sources of errors introduce variations in the change images, the images do reflect the field observations

Space is the machine: a configurational theory of architecture

Since The social logic of space was published in 1984, Bill Hillier and his colleagues at University College London have been conducting research on how space features in the form and functioning of buildings and cities. A key outcome is the concept of ‘spatial configuration’ — meaning relations which take account of other relations in a complex. New techniques have been developed and applied to a wide range of architectural and urban problems. The aim of this book is to assemble some of this work and show how it leads the way to a new type of theory of architecture: an ‘analytic’ theory in which understanding and design advance together. The success of configurational ideas in bringing to light the spatial logic of buildings and cities suggests that it might be possible to extend these ideas to other areas of the human sciences where problems of configuration and pattern are critical