Active Contour-Based Segmentation of Head and Neck with Adaptive Atlas Selection

This paper presents automated segmentation of structuresin the Head and Neck (H\&N) region, using an activecontour-based joint registration and segmentation model.A new atlas selection strategy is also used. Segmentationis performed based on the dense deformation fieldcomputed from the registration of selected structures inthe atlas image that have distinct boundaries, onto thepatient's image. This approach results in robustsegmentation of the structures of interest, even in thepresence of tumors, or anatomical differences between theatlas and the patient image. For each patient, an atlasimage is selected from the available atlas-database,based on the similarity metric value, computed afterperforming an affine registration between each image inthe atlas-database and the patient's image. Unlike manyof the previous approaches in the literature, thesimilarity metric is not computed over the entire imageregion; rather, it is computed only in the regions ofsoft tissue structures to be segmented. Qualitative andquantitative evaluation of the results is presented.

An embedded real-time red peach detection system based on an OV7670 camera, ARM Cortex-M4 processor and 3D Look-Up Tables

This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera. The future goal of this embedded vision system will be to control a robotized arm to automatically select and pick some fruit directly from the tree. The complete embedded system has been designed to be placed directly in the gripper tool of the future robotized harvesting arm. The embedded system will be able to perform real-time fruit detection and tracking by using a three-dimensional look-up-table (LUT) defined in the RGB color space and optimized for fruit picking. Additionally, two different methodologies for creating optimized 3D LUTs based on existing linear color models and fruit histograms were implemented in this work and compared for the case of red peaches. The resulting system is able to acquire general and zoomed orchard images and to update the relative tracking information of a red peach in the tree ten times per second.

The role of gadoxetic acid as a paramagnetic contrast medium in the characterization and detection of focal liver lesions: a review

Recent studies have demonstrated that the use of paramagnetic hepatobiliary contrast agents in the acquisition of magnetic resonance images remarkably improves the detection and differentiation of focal liver lesions, as compared with extracellular contrast agents. Paramagnetic hepatobiliary contrast agents initially show the perfusion of the lesions, as do extracellular agents, but delayed contrast-enhanced images can demonstrate contrast uptake by functional hepatocytes, providing further information for a better characterization of the lesions. Additionally, this intrinsic characteristic increases the accuracy in the detection of hepatocellular carcinomas and metastases, particularly the small-sized ones. Recently, a hepatobiliary contrast agent called gadolinium ethoxybenzyl dimeglumine, that is simply known as gadoxetic acid, was approved by the National Health Surveillance Agency for use in humans. The authors present a literature review and a practical approach of magnetic resonance imaging utilizing gadoxetic acid as contrast agent, based on patients' images acquired during their initial experiment.

From Regional Landslide Detection to Site-Specific Slope Deformation Monitoring and Modelling Based on Active Remote Sensors

Landslide processes can have direct and indirect consequences affecting human lives and activities. In order to improve landslide risk management procedures, this PhD thesis aims to investigate capabilities of active LiDAR and RaDAR sensors for landslides detection and characterization at regional scales, spatial risk assessment over large areas and slope instabilities monitoring and modelling at site-specific scales. At regional scales, we first demonstrated recent boat-based mobile LiDAR capabilities to model topography of the Normand coastal cliffs. By comparing annual acquisitions, we validated as well our approach to detect surface changes and thus map rock collapses, landslides and toe erosions affecting the shoreline at a county scale. Then, we applied a spaceborne InSAR approach to detect large slope instabilities in Argentina. Based on both phase and amplitude RaDAR signals, we extracted decisive information to detect, characterize and monitor two unknown extremely slow landslides, and to quantify water level variations of an involved close dam reservoir. Finally, advanced investigations on fragmental rockfall risk assessment were conducted along roads of the Val de Bagnes, by improving approaches of the Slope Angle Distribution and the FlowR software. Therefore, both rock-mass-failure susceptibilities and relative frequencies of block propagations were assessed and rockfall hazard and risk maps could be established at the valley scale. At slope-specific scales, in the Swiss Alps, we first integrated ground-based InSAR and terrestrial LiDAR acquisitions to map, monitor and model the Perraire rock slope deformation. By interpreting both methods individually and originally integrated as well, we therefore delimited the rockslide borders, computed volumes and highlighted non-uniform translational displacements along a wedge failure surface. Finally, we studied specific requirements and practical issues experimented on early warning systems of some of the most studied landslides worldwide. As a result, we highlighted valuable key recommendations to design new reliable systems; in addition, we also underlined conceptual issues that must be solved to improve current procedures. To sum up, the diversity of experimented situations brought an extensive experience that revealed the potential and limitations of both methods and highlighted as well the necessity of their complementary and integrated uses.

Determination of sugars and organic acids in pulp samples by capillary electrophoresis with UV detection

This MSc work was done in the project of BIOMECON financed by Tekes. The prime target of the research was, to develop methods for separation and determination of carbohydrates (sugars), sugar acids and alcohols, and some other organic acids in hydrolyzed pulp samples by capillary electrophoresis (CE) using UV detection. Aspen, spruce, and birch pulps are commonly used for production of papers in Finland. Feedstock components in pulp predominantly consist of carbohydrates, organic acids, lignin, extractives, and proteins. Here in this study, pulps have been hydrolyzed in analytical chemistry laboratories of UPM Company and Lappeenranta University in order to convert them into sugars, acids, alcohols, and organic acids. Foremost objective of this study was to quantify and identify the main and by-products in the pulp samples. For the method development and optimization, increased precision in capillary electrophoresis was accomplished by calculating calibration data of 16 analytes such as D-(-)-fructose, D(+)-xylose, D(+)-mannose, D(+)-cellobiose, D-(+)-glucose, D-(+)-raffinose, D(-)-mannitol, sorbitol, rhamnose, sucrose, xylitol, galactose, maltose, arabinose, ribose, and, α-lactose monohydratesugars and 16 organic acids such as D-glucuronic, oxalic, acetic, propionic, formic, glycolic, malonic, maleic, citric, L-glutamic, tartaric, succinic, adipic, ascorbic, galacturonic, and glyoxylic acid. In carbohydrate and polyalcohol analyses, the experiments with CE coupled to direct UV detection and positive separation polarity was performed in 36 mM disodium hydrogen phosphate electrolyte solution. For acid analyses, CE coupled indirect UV detection, using negative polarity, and electrolyte solution made of 2,3 pyridinedicarboxylic acid, Ca2+ salt, Mg2+ salts, and myristyltrimethylammonium hydroxide in water was used. Under optimized conditions, limits of detection, relative standard deviations and correlation coefficients of each compound were measured. The optimized conditions were used for the identification and quantification of carbohydrates and acids produced by hydrolyses of pulp. The concentrations of the analytes varied between 1 mg – 0.138 g in liter hydrolysate.

First-Line Chemotherapy with Anthracycline and Taxane Combination in Metastatic Breast Cancer. Detection of bone metastases with TRACP 5b

Background: In Finland, breast cancer (BC) is the most common cancer among women, and prostate cancer (PC) that among men. At the metastatic stage both cancers remain essentially incurable. The goals of therapy include palliation of symptoms, improvement or maintenance of quality of life (QoL), delay of disease progression, and prolongation of survival. Balancing between efficacy and toxicity is the major challenge. With increasing costs of new treatments, appropriate use of resources is paramount. When new treatment regimes are introduced into clinical practice a comprehensive assessment of clinical benefit, adverse effects and cost is necessary. Both BC and PC show a predilection to metastasize to bone. Bone metastases cause significant morbidity impairing the patients´ QoL. Diagnosis of bone metastases relies mainly on radiological methods, which however lack optimal sensitivity and specificity. New tools are needed for detection and follow-up of bone metastases. Aims: Anthracyclines and taxanes are effective chemotherapeutic agents in the treatment of metastatic breast cancer (MBC) with different mechanisms of action. Therefore, evaluation of the combination of anthracyclines with taxanes was a justifiable approach in the treatment of MBC patients. We assessed the efficacy, toxicity, cost of treatment and QoL of BC patients treated with first-line chemotherapy for metastatic disease with the combination epirubicin and docetaxel. We also evaluated the diagnostic potential of tartrate-resistant acid phosphatase 5b (TRACP 5b) and carboxyterminal telopeptides of type I collagen (ICTP) in the diagnosis of bone metastases in BC and TRACP 5b in PC patients. Results: The combination of epirubicin and docetaxel was effective in this phase II study, but required individual dose adjustment to avoid neutropenic infections, and the use of growth factors to maintain a feasible dose level. The response rate was 54 % (95 % CI 37-71) and the median overall survival (OS) was 26 months. Of the patients, 87 % were treated for infections. The treatment of adverse events required additional use of health resources mainly due to neutropenic infections, thereby raising direct treatment costs by 20 %. Despite adverse events, the global QoL was not significantly compromised during the treatment. Clinically evident acute cardiac toxicity was not observed. The combination of serum TRACP 5b and ICTP was at least equally sensitive and specific in detection of of bone metastases as commonly used total alkaline phosphatise (tALP) in BC patients. In contrast, TRACP 5b was less specific and sensitive than tALP as a marker of skeletal changes in PC patients. Conclusions: Treatment with epirubicin and docetaxel showed high efficacy in first-line chemotherapy of MBC. The relatively high incidence of neutropenic infections requiring hospitalization increased the treatment costs. Despite adverse events, the global QoL of the patients was not significantly compromised. The combination of TRACP 5b and ICTP showed similar activity as tALP in detecting bone metastases in MBC. In contrast, TRACP 5b was less specific and sensitive than tALP as a marker of skeletal changes in PC.

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.

Image segmentation integrating colour, texture and boundary information

La tesis se centra en la Visión por Computador y, más concretamente, en la segmentación de imágenes, la cual es una de las etapas básicas en el análisis de imágenes y consiste en la división de la imagen en un conjunto de regiones visualmente distintas y uniformes considerando su intensidad, color o textura. Se propone una estrategia basada en el uso complementario de la información de región y de frontera durante el proceso de segmentación, integración que permite paliar algunos de los problemas básicos de la segmentación tradicional. La información de frontera permite inicialmente identificar el número de regiones presentes en la imagen y colocar en el interior de cada una de ellas una semilla, con el objetivo de modelar estadísticamente las características de las regiones y definir de esta forma la información de región. Esta información, conjuntamente con la información de frontera, es utilizada en la definición de una función de energía que expresa las propiedades requeridas a la segmentación deseada: uniformidad en el interior de las regiones y contraste con las regiones vecinas en los límites. Un conjunto de regiones activas inician entonces su crecimiento, compitiendo por los píxeles de la imagen, con el objetivo de optimizar la función de energía o, en otras palabras, encontrar la segmentación que mejor se adecua a los requerimientos exprsados en dicha función. Finalmente, todo esta proceso ha sido considerado en una estructura piramidal, lo que nos permite refinar progresivamente el resultado de la segmentación y mejorar su coste computacional. La estrategia ha sido extendida al problema de segmentación de texturas, lo que implica algunas consideraciones básicas como el modelaje de las regiones a partir de un conjunto de características de textura y la extracción de la información de frontera cuando la textura es presente en la imagen. Finalmente, se ha llevado a cabo la extensión a la segmentación de imágenes teniendo en cuenta las propiedades de color y textura. En este sentido, el uso conjunto de técnicas no-paramétricas de estimación de la función de densidad para la descripción del color, y de características textuales basadas en la matriz de co-ocurrencia, ha sido propuesto para modelar adecuadamente y de forma completa las regiones de la imagen. La propuesta ha sido evaluada de forma objetiva y comparada con distintas técnicas de integración utilizando imágenes sintéticas. Además, se han incluido experimentos con imágenes reales con resultados muy positivos.

Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.

Evaluation of probabilistic occupancy map people detection for surveillance systems

In this paper, we evaluate the Probabilistic Occupancy Map (POM) pedestrian detection algorithm on the PETS 2009 benchmark dataset. POM is a multi-camera generative detection method, which estimates ground plane occupancy from multiple background subtraction views. Occupancy probabilities are iteratively estimated by fitting a synthetic model of the background subtraction to the binary foreground motion. Furthermore, we test the integration of this algorithm into a larger framework designed for understanding human activities in real environments. We demonstrate accurate detection and localization on the PETS dataset, despite suboptimal calibration and foreground motion segmentation input.

Towards pain-free diagnosis of skin diseases through multiplexed microneedles: biomarker extraction and detection using a highly sensitive blotting method

Immunodiagnostic microneedles provide a novel way to extract protein biomarkers from the skin in a minimally invasive manner for analysis in vitro. The technology could overcome challenges in biomarker analysis specifically in solid tissue, which currently often involves invasive biopsies. This study describes the development of a multiplex immunodiagnostic device incorporating mechanisms to detect multiple antigens simultaneously, as well as internal assay controls for result validation. A novel detection method is also proposed. It enables signal detection specifically at microneedle tips and therefore may aid the construction of depth profiles of skin biomarkers. The detection method can be coupled with computerised densitometry for signal quantitation. The antigen specificity, sensitivity and functional stability of the device were assessed against a number of model biomarkers. Detection and analysis of endogenous antigens (interleukins 1α and 6) from the skin using the device was demonstrated. The results were verified using conventional enzyme-linked immunosorbent assays. The detection limit of the microneedle device, at ≤10 pg/mL, was at least comparable to conventional plate-based solid-phase enzyme immunoassays.

Detection of urease in the cell wall and membranes from leaf tissues of bromeliad species

Urea is an important nitrogen source for some bromeliad species, and in nature it is derived from the excretion of amphibians, which visit or live inside the tank water. Its assimilation is dependent on the hydrolysis by urease (EC: 3.5.1.5), and although this enzyme has been extensively studied to date, little information is available about its cellular location. In higher plants, this enzyme is considered to be present in the cytoplasm. However, there is evidence that urease is secreted by the bromeliad Vriesea gigantea, implying that this enzyme is at least temporarily located in the plasmatic membrane and cell wall. In this article, urease activity was measured in different cell fractions using leaf tissues of two bromeliad species: the tank bromeliad V. gigantea and the terrestrial bromeliad Ananas comosus (L.) Merr. In both species, urease was present in the cell wall and membrane fractions, besides the cytoplasm. Moreover, a considerable difference was observed between the species: while V. gigantea had 40% of the urease activity detected in the membranes and cell wall fractions, less than 20% were found in the same fractions in A. comosus. The high proportion of urease found in cell wall and membranes in V. gigantea was also investigated by cytochemical detection and immunoreaction assay. Both approaches confirmed the enzymatic assay. We suggest this physiological characteristic allows tank bromeliads to survive in a nitrogen-limited environment, utilizing urea rapidly and efficiently and competing successfully for this nitrogen source against microorganisms that live in the tank water.

Motion Detection for Video Surveillance

This thesis is related to the broad subject of automatic motion detection and analysis in videosurveillance image sequence. Besides, proposing the new unique solution, some of the previousalgorithms are evaluated, where some of the approaches are noticeably complementary sometimes.In real time surveillance, detecting and tracking multiple objects and monitoring their activities inboth outdoor and indoor environment are challenging task for the video surveillance system. Inpresence of a good number of real time problems limits scope for this work since the beginning. Theproblems are namely, illumination changes, moving background and shadow detection.An improved background subtraction method has been followed by foreground segmentation, dataevaluation, shadow detection in the scene and finally the motion detection method. The algorithm isapplied on to a number of practical problems to observe whether it leads us to the expected solution.Several experiments are done under different challenging problem environment. Test result showsthat under most of the problematic environment, the proposed algorithm shows the better qualityresult.

Detection of SPM and IMP metallo-beta-lactamases in clinical specimens of Pseudomonas aeruginosa from a Brazilian public tertiary hospital

Phenotypic and genotypic SPM and IMP metallo-beta-lactamases (MBL) detection and also the determination of minimal inhibitory concentrations (MIC) to imipenem, meropenem and ceftazidime were evaluated in 47 multidrug-resistant Pseudomonas aeruginosa isolates from clinical specimens. Polymerase chain reaction detected 14 positive samples to either bla(SPM) or bla(IMP) genes, while the best phenotypic assay (ceftazidime substrate and mercaptopropionic acid inhibitor) detected 13 of these samples. Imipenem, meropenem and ceftazidime MICs were higher for MBL positive compared to MBL negative isolates. We describe here the SPM and IMP MBL findings in clinical specimens of P. aeruginosa from the University Hospital of Botucatu Medical School, São Paulo, Brazil, that reinforce local studies showing the high spreading of bla(SPM) and bla(IMP) genes among Brazilian clinical isolates.

Reproducibility and accuracy of the ICDAS-II for occlusal caries detection

Objectives:The aim of this in vitro study was to assess the inter- and intra-examiner reproducibility and the accuracy of the International Caries Detection and Assessment System-II (ICDAS-II) in detecting occlusal caries.Methods:One hundred and sixty-three molars were independently assessed twice by two experienced dentists using the 0- to 6-graded ICDAS-II. The teeth were histologically prepared and classified using two different histological systems [Ekstrand et al. (1997) Caries Research vol. 31, pp. 224-231; Lussi et al. (1999) Caries Research vol. 33, pp. 261-266] and assessed for caries extension. Sensitivity, specificity, accuracy and area under the ROC curve (A(z)) were obtained at D(2) and D(3) thresholds. Unweighted kappa coefficient was used to assess inter- and intra-examiner reproducibility.Results:For the Ekstrand et al. histological classification the sensitivity was 0.99 and 1.00, specificity 1.00 and 0.69 and accuracy 0.99 and 0.76 at D(2) and D(3), respectively. For the Lussi et al. histological classification the sensitivity was 0.91 and 0.75, specificity 0.47 and 0.62 and accuracy 0.86 and 0.68 at D(2) and D(3), respectively. The A(z) varied from 0.54 to 0.73. The inter- and intra-examiner kappa values were 0.51 and 0.58, respectively.Conclusions:ICDAS-II presented good reproducibility and accuracy in detecting occlusal caries, especially caries lesions in the outer half of the enamel.