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.

The influence of crystallization conditions on the filtration characteristics of sulphathiazole suspensions

Cooling crystallization is one of the most important purification and separation techniques in the chemical and pharmaceutical industry. The product of the cooling crystallization process is always a suspension that contains both the mother liquor and the product crystals, and therefore the first process step following crystallization is usually solid-liquid separation. The properties of the produced crystals, such as their size and shape, can be affected by modifying the conditions during the crystallization process. The filtration characteristics of solid/liquid suspensions, on the other hand, are strongly influenced by the particle properties, as well as the properties of the liquid phase. It is thus obvious that the effect of the changes made to the crystallization parameters can also be seen in the course of the filtration process. Although the relationship between crystallization and filtration is widely recognized, the number of publications where these unit operations have been considered in the same context seems to be surprisingly small. This thesis explores the influence of different crystallization parameters in an unseeded batch cooling crystallization process on the external appearance of the product crystals and on the pressure filtration characteristics of the obtained product suspensions. Crystallization experiments are performed by crystallizing sulphathiazole (C9H9N3O2S2), which is a wellknown antibiotic agent, from different mixtures of water and n-propanol in an unseeded batch crystallizer. The different crystallization parameters that are studied are the composition of the solvent, the cooling rate during the crystallization experiments carried out by using a constant cooling rate throughout the whole batch, the cooling profile, as well as the mixing intensity during the batch. The obtained crystals are characterized by using an automated image analyzer and the crystals are separated from the solvent through constant pressure batch filtration experiments. Separation characteristics of the suspensions are described by means of average specific cake resistance and average filter cake porosity, and the compressibilities of the cakes are also determined. The results show that fairly large differences can be observed between the size and shape of the crystals, and it is also shown experimentally that the changes in the crystal size and shape have a direct impact on the pressure filtration characteristics of the crystal suspensions. The experimental results are utilized to create a procedure that can be used for estimating the filtration characteristics of solid-liquid suspensions according to the particle size and shape data obtained by image analysis. Multilinear partial least squares regression (N-PLS) models are created between the filtration parameters and the particle size and shape data, and the results presented in this thesis show that relatively obvious correlations can be detected with the obtained models.

A Farewell to Flat Biology. Three-dimensional Cell Culture Models in Cancer Drug Target Identification and Validation

Cells of epithelial origin, e.g. from breast and prostate cancers, effectively differentiate into complex multicellular structures when cultured in three-dimensions (3D) instead of conventional two-dimensional (2D) adherent surfaces. The spectrum of different organotypic morphologies is highly dependent on the culture environment that can be either non-adherent or scaffold-based. When embedded in physiological extracellular matrices (ECMs), such as laminin-rich basement membrane extracts, normal epithelial cells differentiate into acinar spheroids reminiscent of glandular ductal structures. Transformed cancer cells, in contrast, typically fail to undergo acinar morphogenic patterns, forming poorly differentiated or invasive multicellular structures. The 3D cancer spheroids are widely accepted to better recapitulate various tumorigenic processes and drug responses. So far, however, 3D models have been employed predominantly in the Academia, whereas the pharmaceutical industry has yet to adopt a more widely and routine use. This is mainly due to poor characterisation of cell models, lack of standardised workflows and high throughput cell culture platforms, and the availability of proper readout and quantification tools. In this thesis, a complete workflow has been established entailing well-characterised 3D cell culture models for prostate cancer, a standardised 3D cell culture routine based on high-throughput-ready platform, automated image acquisition with concomitant morphometric image analysis, and data visualisation, in order to enable large-scale high-content screens. Our integrated suite of software and statistical analysis tools were optimised and validated using a comprehensive panel of prostate cancer cell lines and 3D models. The tools quantify multiple key cancer-relevant morphological features, ranging from cancer cell invasion through multicellular differentiation to growth, and detect dynamic changes both in morphology and function, such as cell death and apoptosis, in response to experimental perturbations including RNA interference and small molecule inhibitors. Our panel of cell lines included many non-transformed and most currently available classic prostate cancer cell lines, which were characterised for their morphogenetic properties in 3D laminin-rich ECM. The phenotypes and gene expression profiles were evaluated concerning their relevance for pre-clinical drug discovery, disease modelling and basic research. In addition, a spontaneous model for invasive transformation was discovered, displaying a highdegree of epithelial plasticity. This plasticity is mediated by an abundant bioactive serum lipid, lysophosphatidic acid (LPA), and its receptor LPAR1. The invasive transformation was caused by abrupt cytoskeletal rearrangement through impaired G protein alpha 12/13 and RhoA/ROCK, and mediated by upregulated adenylyl cyclase/cyclic AMP (cAMP)/protein kinase A, and Rac/ PAK pathways. The spontaneous invasion model tangibly exemplifies the biological relevance of organotypic cell culture models. Overall, this thesis work underlines the power of novel morphometric screening tools in drug discovery.

Differentiation of Metabolically Distinct Areas within Head and Neck Region using Dynamic 18F-FDG Positron Emission Tomography Imaging

Positron Emission Tomography (PET) using ¹⁸F-FDG is playing a vital role in the diagnosis and treatment planning of cancer. However, the most widely used radiotracer, ¹⁸F-FDG, is not specific for tumours and can also accumulate in inflammatory lesions as well as normal physiologically active tissues making diagnosis and treatment planning complicated for the physicians. Malignant, inflammatory and normal tissues are known to have different pathways for glucose metabolism which could possibly be evident from different characteristics of the time activity curves from a dynamic PET acquisition protocol. Therefore, we aimed to develop new image analysis methods, for PET scans of the head and neck region, which could differentiate between inflammation, tumour and normal tissues using this functional information within these radiotracer uptake areas. We developed different dynamic features from the time activity curves of voxels in these areas and compared them with the widely used static parameter, SUV, using Gaussian Mixture Model algorithm as well as K-means algorithm in order to assess their effectiveness in discriminating metabolically different areas. Moreover, we also correlated dynamic features with other clinical metrics obtained independently of PET imaging. The results show that some of the developed features can prove to be useful in differentiating tumour tissues from inflammatory regions and some dynamic features also provide positive correlations with clinical metrics. If these proposed methods are further explored then they can prove to be useful in reducing false positive tumour detections and developing real world applications for tumour diagnosis and contouring.

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

X-Ray analysis to assess mechanical damage in sweet corn seeds

The X-ray test is a precise, fast and non-destructive method to detect mechanical damage in seeds. In the present study, the efficiency of X-ray analysis in identifying the extent of mechanical damage in sweet corn seeds and its relationship with germination and vigor was evaluated. Hybrid 'SWB 551' (sh2) seeds with round (R) and flat (F) shapes were classified as large (L), medium (M1, M2 and M3) and small (S), using sieves with round and oblong screens. After artificial exposure to different levels of damage (0, 1, 3, 5 and 7 impacts), seeds were X-rayed (15 kV, 5 min) and submitted to germination (25 °C/5 days) and cold (10 °C/7 days) tests. Digital images of normal and abnormal seedlings and ungerminated seeds from germination and cold tests were jointly analyzed with the seed X-ray images. Results showed that damage affecting the embryonic axis resulted in abnormal seedlings or dead seeds in the germination and cold tests. The X-ray analysis is efficient for identifying mechanical damage in sweet corn seeds, allowing damage severity to be associated with losses in germination and vigor.

Computerized imaging analysis of seedlings for assessment of physiological potential of wheat seeds

Nowadays, image analysis is one of the most modern tools in evaluating physiological potential of seeds. This study aimed at verifying the efficiency of the seedling imaging analysis to assess physiological potential of wheat seeds. The seeds of wheat, cultivars IAC 370 and IAC 380, each of which represented by five different lots, were stored during four months under natural environmental conditions of temperature (T) and relative humidity (RH), in municipality of Piracicaba, Stated of São Paulo, Brazil. For this, bimonthly assessments were performed to quantify moisture content and physiological potential of seeds by means of tests of: germination, first count, accelerated aging, electrical conductivity, seedling emergence, and computerized analysis of seedlings, using the Seed Vigor Imaging System (SVIS®). It has been concluded that the computerized analyses of seedling through growth indexes and vigor, using the SVIS®, is efficient to assess physiological potential of wheat seeds.

The development of automated methods for the determination of trace concentrations of carbomate pesticides in water using solid sorbent pre-concentration methods and high performance liquid chromatography /

Several automated reversed-phase HPLC methods have been developed to determine trace concentrations of carbamate pesticides (which are of concern in Ontario environmental samples) in water by utilizing two solid sorbent extraction techniques. One of the methods is known as on-line pre-concentration'. This technique involves passing 100 milliliters of sample water through a 3 cm pre-column, packed with 5 micron ODS sorbent, at flow rates varying from 5-10 mUmin. By the use of a valve apparatus, the HPLC system is then switched to a gradient mobile phase program consisting of acetonitrile and water. The analytes, Propoxur, Carbofuran, Carbaryl, Propham, Captan, Chloropropham, Barban, and Butylate, which are pre-concentrated on the pre-column, are eluted and separated on a 25 cm C-8 analytical column and determined by UV absorption at 220 nm. The total analytical time is 60 minutes, and the pre-column can be used repeatedly for the analysis of as many as thirty samples. The method is highly sensitive as 100 percent of the analytes present in the sample can be injected into the HPLC. No breakthrough of any of the analytes was observed and the minimum detectable concentrations range from 10 to 480 ng/L. The developed method is totally automated for the analysis of one sample. When the above mobile phase is modified with a buffer solution, Aminocarb, Benomyl, and its degradation product, MBC, can also be detected along with the above pesticides with baseline resolution for all of the analytes. The method can also be easily modified to determine Benomyl and MBC both as solute and as particulate matter. By using a commercially available solid phase extraction cartridge, in lieu of a pre-column, for the extraction and concentration of analytes, a completely automated method has been developed with the aid of the Waters Millilab Workstation. Sample water is loaded at 10 mL/min through a cartridge and the concentrated analytes are eluted from the sorbent with acetonitrile. The resulting eluate is blown-down under nitrogen, made up to volume with water, and injected into the HPLC. The total analytical time is 90 minutes. Fifty percent of the analytes present in the sample can be injected into the HPLC, and recoveries for the above eight pesticides ranged from 84 to 93 percent. The minimum detectable concentrations range from 20 to 960 ng/L. The developed method is totally automated for the analysis of up to thirty consecutive samples. The method has proven to be applicable to both purer water samples as well as untreated lake water samples.

Segmentation d’images intravasculaires ultrasonores

L'imagerie intravasculaire ultrasonore (IVUS) est une technologie médicale par cathéter qui produit des images de coupe des vaisseaux sanguins. Elle permet de quantifier et d'étudier la morphologie de plaques d'athérosclérose en plus de visualiser la structure des vaisseaux sanguins (lumière, intima, plaque, média et adventice) en trois dimensions. Depuis quelques années, cette méthode d'imagerie est devenue un outil de choix en recherche aussi bien qu'en clinique pour l'étude de la maladie athérosclérotique. L'imagerie IVUS est par contre affectée par des artéfacts associés aux caractéristiques des capteurs ultrasonores, par la présence de cônes d'ombre causés par les calcifications ou des artères collatérales, par des plaques dont le rendu est hétérogène ou par le chatoiement ultrasonore (speckle) sanguin. L'analyse automatisée de séquences IVUS de grande taille représente donc un défi important. Une méthode de segmentation en trois dimensions (3D) basée sur l'algorithme du fast-marching à interfaces multiples est présentée. La segmentation utilise des attributs des régions et contours des images IVUS. En effet, une nouvelle fonction de vitesse de propagation des interfaces combinant les fonctions de densité de probabilité des tons de gris des composants de la paroi vasculaire et le gradient des intensités est proposée. La segmentation est grandement automatisée puisque la lumière du vaisseau est détectée de façon entièrement automatique. Dans une procédure d'initialisation originale, un minimum d'interactions est nécessaire lorsque les contours initiaux de la paroi externe du vaisseau calculés automatiquement sont proposés à l'utilisateur pour acceptation ou correction sur un nombre limité d'images de coupe longitudinale. La segmentation a été validée à l'aide de séquences IVUS in vivo provenant d'artères fémorales provenant de différents sous-groupes d'acquisitions, c'est-à-dire pré-angioplastie par ballon, post-intervention et à un examen de contrôle 1 an suivant l'intervention. Les résultats ont été comparés avec des contours étalons tracés manuellement par différents experts en analyse d'images IVUS. Les contours de la lumière et de la paroi externe du vaisseau détectés selon la méthode du fast-marching sont en accord avec les tracés manuels des experts puisque les mesures d'aire sont similaires et les différences point-à-point entre les contours sont faibles. De plus, la segmentation par fast-marching 3D s'est effectuée en un temps grandement réduit comparativement à l'analyse manuelle. Il s'agit de la première étude rapportée dans la littérature qui évalue la performance de la segmentation sur différents types d'acquisition IVUS. En conclusion, la segmentation par fast-marching combinant les informations des distributions de tons de gris et du gradient des intensités des images est précise et efficace pour l'analyse de séquences IVUS de grandes tailles. Un outil de segmentation robuste pourrait devenir largement répandu pour la tâche ardue et fastidieuse qu'est l'analyse de ce type d'images.

Développement et caractérisation d’une méthode photonique pour créer des distributions spatiales de protéines

Les cellules sont capables de détecter les distributions spatiales de protéines et ainsi de migrer ou s’étendre dans la direction appropriée. Une compréhension de la réponse cellulaire aux modifications de ces distributions spatiales de protéines est essentielle pour l’avancement des connaissances dans plusieurs domaines de recherches tels que le développement, l’immunologie ou l’oncologie. Un exemple particulièrement complexe est le guidage d’axones se déroulant pendant le développement du système nerveux. Ce dernier nécessite la présence de plusieurs distributions de molécules de guidages étant attractives ou répulsives pour connecter correctement ce réseau complexe qu’est le système nerveux. Puisque plusieurs indices de guidage collaborent, il est particulièrement difficile d’identifier la contribution individuelle ou la voie de signalisation qui est déclenchée in vivo, il est donc nécessaire d’utiliser des méthodes pour reproduire ces distributions de protéines in vitro. Plusieurs méthodes existent pour produire des gradients de protéines solubles ou liées aux substrats. Quelques méthodes pour produire des gradients solubles sont déjà couramment utilisées dans plusieurs laboratoires, mais elles limitent l’étude aux distributions de protéines qui sont normalement sécrétées in vivo. Les méthodes permettant de produire des distributions liées au substrat sont particulièrement complexes, ce qui restreint leur utilisation à quelques laboratoires. Premièrement, nous présentons une méthode simple qui exploite le photoblanchiment de molécules fluorescentes pour créer des motifs de protéines liées au substrat : Laser-assisted protein adsorption by photobleaching (LAPAP). Cette méthode permet de produire des motifs de protéines complexes d’une résolution micrométrique et d’une grande portée dynamique. Une caractérisation de la technique a été faite et en tant que preuve de fonctionnalité, des axones de neurones du ganglion spinal ont été guidés sur des gradients d’un peptide provenant de la laminine. Deuxièmement, LAPAP a été amélioré de manière à pouvoir fabriquer des motifs avec plusieurs composantes grâce à l’utilisation de lasers à différentes longueurs d’onde et d’anticorps conjugués à des fluorophores correspondants à ces longueurs d’onde. De plus, pour accélérer et simplifier le processus de fabrication, nous avons développé LAPAP à illumination à champ large qui utilise un modulateur spatial de lumière, une diode électroluminescente et un microscope standard pour imprimer directement un motif de protéines. Cette méthode est particulièrement simple comparativement à la version originale de LAPAP puisqu’elle n’implique pas le contrôle de la puissance laser et de platines motorisées, mais seulement d’envoyer l’image du motif désiré au modulateur spatial. Finalement, nous avons utilisé LAPAP pour démontrer que notre technique peut être utilisée dans des analyses de haut contenu pour quantifier les changements morphologiques résultant de la croissance neuronale sur des gradients de protéines de guidage. Nous avons produit des milliers de gradients de laminin-1 ayant différentes pentes et analysé les variations au niveau du guidage de neurites provenant d’une lignée cellulaire neuronale (RGC-5). Un algorithme pour analyser les images des cellules sur les gradients a été développé pour détecter chaque cellule et quantifier la position du centroïde du soma ainsi que les angles d’initiation, final et de braquage de chaque neurite. Ces données ont démontré que les gradients de laminine influencent l’angle d’initiation des neurites des RGC-5, mais n’influencent pas leur braquage. Nous croyons que les résultats présentés dans cette thèse faciliteront l’utilisation de motifs de protéines liées au substrat dans les laboratoires des sciences de la vie, puisque LAPAP peut être effectué à l’aide d’un microscope confocal ou d’un microscope standard légèrement modifié. Cela pourrait contribuer à l’augmentation du nombre de laboratoires travaillant sur le guidage avec des gradients liés au substrat afin d’atteindre la masse critique nécessaire à des percées majeures en neuroscience.

Automatic Optic Disc Boundary Extraction from Color Fundus Images

Efficient optic disc segmentation is an important task in automated retinal screening. For the same reason optic disc detection is fundamental for medical references and is important for the retinal image analysis application. The most difficult problem of optic disc extraction is to locate the region of interest. Moreover it is a time consuming task. This paper tries to overcome this barrier by presenting an automated method for optic disc boundary extraction using Fuzzy C Means combined with thresholding. The discs determined by the new method agree relatively well with those determined by the experts. The present method has been validated on a data set of 110 colour fundus images from DRION database, and has obtained promising results. The performance of the system is evaluated using the difference in horizontal and vertical diameters of the obtained disc boundary and that of the ground truth obtained from two expert ophthalmologists. For the 25 test images selected from the 110 colour fundus images, the Pearson correlation of the ground truth diameters with the detected diameters by the new method are 0.946 and 0.958 and, 0.94 and 0.974 respectively. From the scatter plot, it is shown that the ground truth and detected diameters have a high positive correlation. This computerized analysis of optic disc is very useful for the diagnosis of retinal diseases