Urban sprawl analysis and modeling in Asmara, Eritreia: Application of Geospatial Tools

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Pore space analysis of beech wood: The vessel network

Water transport in wood is vital for the survival of trees. With synchrotron radiation X-ray tomographic microscopy (SRXTM), it has become possible to characterize and quantify the three-dimensional (3D) network formed by vessels that are responsible for longitudinal transport. In the present study, the spatial size dependence of vessels and the organization inside single growth rings in terms of vessel-induced porosity was studied by SRXTM. Network characteristics, such as connectivity, were deduced by digital image analysis from the processed tomographic data and related to known complex network topologies.

Morphometric Analysis of McCoy Cells Inoculated with Cerebrospinal Fluid from Patients with Rabies

To demonstrate the potential of McCoy cells for the isolation of rabies virus from the cerebrospinal (CSF) fluid of a patient with a diagnosis of rabies, McCoy cells were inoculated with CSF from a patient with a clinical diagnosis of rabies and investigated in terms of morphometric aspect using the JAVA analysis system for the quantification of the increased size of infected cells compared to noninfected cells. The cells were also examined in terms of specific staining for the diagnosis of rabies by the method of Sellers for the observation of intracytoplasmic inclusions and by specific immunofluorescence staining for rabies virus. Infected cells showed changes in cell permeability and morphologic modifications which differed significantly compared to normal cells (P<0.001) when analyzed by the Mann-Whitney and Kruskal-Wallis tests. Intense activity of the endoplasmic reticulum was also observed, as indicated by the presence of intracytoplasmic inclusions visualized by specific staining. The present study demonstrated the isolation of rabies virus from the CSF of a patient with rabies, showing that McCoy cells can be used for the laboratory diagnosis of patients suspected to have rabies.

Morphologic and morphometric analysis of Hepatozoon spp. (Apicomplexa, Hepatozoidae) of snakes

Hepatozoon species are the most abundant hemoparasites of snakes. Its identification has been based mainly on the morphologic characterization of the gamonts in the peripheral blood of the vertebrate host and also of the cysts found in the internal organs of the vertebrate and invertebrate hosts. Using a computerized image analysis system, we studied five species of Hepatozoon from recently captured snakes in Botucatu, State of São Paulo, Brazil, to evaluate the importance of the morphology and morphometry of the gamonts for the characterization of Hepatozoon species and to analyze the morphologic changes induced in the erythrocytes by the parasite. The studied species were H. terzii of Boa constrictor amarali, Hepatozoon sp. of Crotalus durissusterrificus, H. philodryasi of Philodryas patagoniensis, and H. migonei and H. cyclagrasi of Hydrodynastes gigas. We observed three different groups, one of them including the species H. terzii, H. philodryasi and Hepatozoon sp. of C. durissus terrificus; and the other two consisting of H. migonei and H. cyclagrasi. Degree of alterations in the erythrocytes was variable and it may be useful for characterization of Hepatozoon species.

Compression-based Image Registration

Image registration is an important component of image analysis used to align two or more images. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that the image registration process can be dealt with from the perspective of a compression problem. Second, we demonstrate that the similarity metric, introduced by Li et al., performs well in image registration. Two different versions of the similarity metric have been used: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images

Sexual dimorphism of the human mandible: demonstration by elliptical Fourier analysis.

A new quantitative approach of the mandibular sexual dimorphism, based on computer-aided image analysis and elliptical Fourier analysis of the mandibular outline in lateral view is presented. This method was applied to a series of 117 dentulous mandibles from 69 male and 48 female individuals native of Rhenish countries. Statistical discriminant analysis of the elliptical Fourier harmonics allowed the demonstration of a significant sexual dimorphism in 97.1% of males and 91.7% of females, i.e. in a higher proportion than in previous studies using classical metrical approaches. This original method opens interesting perspectives for increasing the accuracy of sex identification in current anthropological practice and in forensic procedures.

Deoxyribonucleic acid content as an indicator of progression of squamous cell carcinogenesis in the esophagus: comparative analysis on imprint-cytospin and tissue section preparation.

BACKGROUND: The purpose of this study was to explore the potential use of image analysis on tissue sections preparation as a predictive marker of early malignant changes during squamous cell (SC) carcinogenesis in the esophagus. Results of DNA ploidy quantification on formalin-fixed, paraffin-embedded tissue using two different techniques were compared: imprint-cytospin and 6 microm thick tissue sections preparation. METHODS: This retrospective study included 26 surgical specimens of squamous cell carcinoma (SCC) from patients who underwent surgery alone at the Department of Surgery in CHUV Hospital in Lausanne between January 1993 and December 2000. We analyzed 53 samples of healthy tissue, 43 tumors and 7 lymph node metastases. RESULTS: Diploid DNA histogram patterns were observed in all histologically healthy tissues, either distant or proximal to the lesion. Aneuploidy was observed in 34 (79%) of 43 carcinomas, namely 24 (75%) of 32 early squamous cell carcinomas and 10 (91%) of 11 advanced carcinomas. DNA content was similar in the different tumor stages, whether patients presented with single or multiple synchronous tumors. All lymph node metastases had similar DNA content as their primary tumor. CONCLUSIONS: Early malignant changes in the esophagus are associated with alteration in DNA content, and aneuploidy tends to correlate with progression of invasive SCC. A very good correlation between imprint-cytospin and tissue section analysis was observed. Although each method used here showed advantages and disadvantages; tissue sections preparation provided useful information on aberrant cell-cycle regulation and helped select the optimal treatment for the individual patient along with consideration of other clinical parameters.

On the convergence of EM-like algorithms for image segmentation using Markov random fields.

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad hoc and may turn out numerically unstable. In this paper, we review three EM-like variants for Markov random field segmentation and compare their convergence properties both at the theoretical and practical levels. We specifically advocate a numerical scheme involving asynchronous voxel updating, for which general convergence results can be established. Our experiments on brain tissue classification in magnetic resonance images provide evidence that this algorithm may achieve significantly faster convergence than its competitors while yielding at least as good segmentation results.

Artificially Intelligent and Adaptive Methods for Prediction and Analysis of Superheater Fireside Corrosion in Fluidized Bed Boilers

Superheater corrosion causes vast annual losses for the power companies. With a reliable corrosion prediction method, the plants can be designed accordingly, and knowledge of fuel selection and determination of process conditions may be utilized to minimize superheater corrosion. Growing interest to use recycled fuels creates additional demands for the prediction of corrosion potential. Models depending on corrosion theories will fail, if relations between the inputs and the output are poorly known. A prediction model based on fuzzy logic and an artificial neural network is able to improve its performance as the amount of data increases. The corrosion rate of a superheater material can most reliably be detected with a test done in a test combustor or in a commercial boiler. The steel samples can be located in a special, temperature-controlled probe, and exposed to the corrosive environment for a desired time. These tests give information about the average corrosion potential in that environment. Samples may also be cut from superheaters during shutdowns. The analysis ofsamples taken from probes or superheaters after exposure to corrosive environment is a demanding task: if the corrosive contaminants can be reliably analyzed, the corrosion chemistry can be determined, and an estimate of the material lifetime can be given. In cases where the reason for corrosion is not clear, the determination of the corrosion chemistry and the lifetime estimation is more demanding. In order to provide a laboratory tool for the analysis and prediction, a newapproach was chosen. During this study, the following tools were generated: · Amodel for the prediction of superheater fireside corrosion, based on fuzzy logic and an artificial neural network, build upon a corrosion database developed offuel and bed material analyses, and measured corrosion data. The developed model predicts superheater corrosion with high accuracy at the early stages of a project. · An adaptive corrosion analysis tool based on image analysis, constructedas an expert system. This system utilizes implementation of user-defined algorithms, which allows the development of an artificially intelligent system for thetask. According to the results of the analyses, several new rules were developed for the determination of the degree and type of corrosion. By combining these two tools, a user-friendly expert system for the prediction and analyses of superheater fireside corrosion was developed. This tool may also be used for the minimization of corrosion risks by the design of fluidized bed boilers.

Analysis of Satellite Data for Forest Segmentation

In this work we study the classification of forest types using mathematics based image analysis on satellite data. We are interested in improving classification of forest segments when a combination of information from two or more different satellites is used. The experimental part is based on real satellite data originating from Canada. This thesis gives summary of the mathematics basics of the image analysis and supervised learning , methods that are used in the classification algorithm. Three data sets and four feature sets were investigated in this thesis. The considered feature sets were 1) histograms (quantiles) 2) variance 3) skewness and 4) kurtosis. Good overall performances were achieved when a combination of ASTERBAND and RADARSAT2 data sets was used.

Color ad spectral image assessment using novel quality and fidelity techniques

The ongoing development of the digital media has brought a new set of challenges with it. As images containing more than three wavelength bands, often called spectral images, are becoming a more integral part of everyday life, problems in the quality of the RGB reproduction from the spectral images have turned into an important area of research. The notion of image quality is often thought to comprise two distinctive areas – image quality itself and image fidelity, both dealing with similar questions, image quality being the degree of excellence of the image, and image fidelity the measure of the match of the image under study to the original. In this thesis, both image fidelity and image quality are considered, with an emphasis on the influence of color and spectral image features on both. There are very few works dedicated to the quality and fidelity of spectral images. Several novel image fidelity measures were developed in this study, which include kernel similarity measures and 3D-SSIM (structural similarity index). The kernel measures incorporate the polynomial, Gaussian radial basis function (RBF) and sigmoid kernels. The 3D-SSIM is an extension of a traditional gray-scale SSIM measure developed to incorporate spectral data. The novel image quality model presented in this study is based on the assumption that the statistical parameters of the spectra of an image influence the overall appearance. The spectral image quality model comprises three parameters of quality: colorfulness, vividness and naturalness. The quality prediction is done by modeling the preference function expressed in JNDs (just noticeable difference). Both image fidelity measures and the image quality model have proven to be effective in the respective experiments.

Analysis of trophic responses in lesioned brain: focus on basic fibroblast growth factor mechanisms

The actions of fibroblast growth factors (FGFs), particularly the basic form (bFGF), have been described in a large number of cells and include mitogenicity, angiogenicity and wound repair. The present review discusses the presence of the bFGF protein and messenger RNA as well as the presence of the FGF receptor messenger RNA in the rodent brain by means of semiquantitative radioactive in situ hybridization in combination with immunohistochemistry. Chemical and mechanical injuries to the brain trigger a reduction in neurotransmitter synthesis and neuronal death which are accompanied by astroglial reaction. The altered synthesis of bFGF following brain lesions or stimulation was analyzed. Lesions of the central nervous system trigger bFGF gene expression by neurons and/or activated astrocytes, depending on the type of lesion and time post-manipulation. The changes in bFGF messenger RNA are frequently accompanied by a subsequent increase of bFGF immunoreactivity in astrocytes in the lesioned pathway. The reactive astrocytes and injured neurons synthesize increased amount of bFGF, which may act as a paracrine/autocrine factor, protecting neurons from death and also stimulating neuronal plasticity and tissue repair

Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presented

Automatic image-based identification of Saimaa ringed seals

The Saimaa ringed seal is one of the most endangered seals in the world. It is a symbol of Lake Saimaa and a lot of effort have been applied to save it. Traditional methods of seal monitoring include capturing the animals and installing sensors on their bodies. These invasive methods for identifying can be painful and affect the behavior of the animals. Automatic identification of seals using computer vision provides a more humane method for the monitoring. This Master's thesis focuses on automatic image-based identification of the Saimaa ringed seals. This consists of detection and segmentation of a seal in an image, analysis of its ring patterns, and identification of the detected seal based on the features of the ring patterns. The proposed algorithm is evaluated with a dataset of 131 individual seals. Based on the experiments with 363 images, 81\% of the images were successfully segmented automatically. Furthermore, a new approach for interactive identification of Saimaa ringed seals is proposed. The results of this research are a starting point for future research in the topic of seal photo-identification.

Radiographic analysis in castor bean seeds (Ricinus communis L.)

Castor bean cropping has great social and economic value, but its production has been affected by factors such as low quality seeds used for sowing. The quick and precise evaluation of seed quality by x-ray test is known as an effective method to evaluate seed lots, but little is known about the interpretation between of the type of radiographic image and the seed quality correlation. The potential of x-ray analysis as a marker of seed physiological quality and as an initial process for the implementation of the use of computer-assisted image analysis was investigated using castor bean seeds of the different cultivars. The seeds were classified according to internal morphology visualized in the radiography and subjected to the germination test, emergency and seedling growth rate. It was possible to identify the different types of internal tissues, morphological and physical damage in castor bean seeds using the x-ray test. Tissues generating translucent images, embryo deformation, or tissues with less than 50% of endosperm reserves or spotted, negatively affected the physiological potential of the seed lots. Radiographic analysis is effective as an instrument to improve castor bean seed lot quality. This non destructive analysis allows the prediction of seedling performance and enabled the selection of high-quality seeds under the standards of a sustainable and precision agriculture