Nonlinear dynamics of the patient’s response to drug effect during general anesthesia

In today’s healthcare paradigm, optimal sedation during anesthesia plays an important role both in patient welfare and in the socio-economic context. For the closed-loop control of general anesthesia, two drugs have proven to have stable, rapid onset times: propofol and remifentanil. These drugs are related to their effect in the bispectral index, a measure of EEG signal. In this paper wavelet time–frequency analysis is used to extract useful information from the clinical signals, since they are time-varying and mark important changes in patient’s response to drug dose. Model based predictive control algorithms are employed to regulate the depth of sedation by manipulating these two drugs. The results of identification from real data and the simulation of the closed loop control performance suggest that the proposed approach can bring an improvement of 9% in overall robustness and may be suitable for clinical practice.

classification and content based retrieval of digital mammograms and placental sonograms

Image processing has been a challenging and multidisciplinary research area since decades with continuing improvements in its various branches especially Medical Imaging. The healthcare industry was very much benefited with the advances in Image Processing techniques for the efficient management of large volumes of clinical data. The popularity and growth of Image Processing field attracts researchers from many disciplines including Computer Science and Medical Science due to its applicability to the real world. In the meantime, Computer Science is becoming an important driving force for the further development of Medical Sciences. The objective of this study is to make use of the basic concepts in Medical Image Processing and develop methods and tools for clinicians’ assistance. This work is motivated from clinical applications of digital mammograms and placental sonograms, and uses real medical images for proposing a method intended to assist radiologists in the diagnostic process. The study consists of two domains of Pattern recognition, Classification and Content Based Retrieval. Mammogram images of breast cancer patients and placental images are used for this study. Cancer is a disaster to human race. The accuracy in characterizing images using simplified user friendly Computer Aided Diagnosis techniques helps radiologists in detecting cancers at an early stage. Breast cancer which accounts for the major cause of cancer death in women can be fully cured if detected at an early stage. Studies relating to placental characteristics and abnormalities are important in foetal monitoring. The diagnostic variability in sonographic examination of placenta can be overlooked by detailed placental texture analysis by focusing on placental grading. The work aims on early breast cancer detection and placental maturity analysis. This dissertation is a stepping stone in combing various application domains of healthcare and technology.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Grey Level Co-Occurrence Matrices: Generalisation And Some New Features

Grey Level Co-occurrence Matrices (GLCM) are one of the earliest techniques used for image texture analysis. In this paper we defined a new feature called trace extracted from the GLCM and its implications in texture analysis are discussed in the context of Content Based Image Retrieval (CBIR). The theoretical extension of GLCM to n-dimensional gray scale images are also discussed. The results indicate that trace features outperform Haralick features when applied to CBIR.

Entwicklung, in-house Validierung und Anwendung des ganzheitlichen Verfahrens Biokristallisation für die Unterscheidung von Weizen-, Möhren- und Apfelproben aus unterschiedlichem Anbau und Verarbeitungsschritten

Die Qualität ökologischer Produkte wird über den Prozess und nicht am Produkt selbst bestimmt. Die zunehmende Nachfrage nach ökologischen Produkten fordert Methoden, die den Prozess am Produkt zeigen (Authentizitätsprüfung). Eine Literaturstudie für die vorliegende Habilitationsschrift ergab, dass ganzheitliche Verfahren sich dazu besonders eignen. Zu solchen ganzheitlichen Verfahren gehört die Biokristallisation. Bei diesem Verfahren kristallisiert eine Mischung aus Probe und CuCl2 auf einer Glasplatte zu einem Bild, das sowohl visuell, als auch computergestützt ausgewertet werden kann. Es wurden zunächst alle Schritte im Labor dokumentiert und entsprechende Standardarbeitsanweisungen erstellt. Mit einem eigens entwickelten Computerprogramm werden die Bedingungen während der Probenaufbereitung und Kristallisation für jede Probe und jedes Bild erfasst. Mit einer Texturanalyse können auch die für diese Arbeiten erstellte große Menge an Bildern ausgewertet und die Ergebnisse statistisch bearbeitet werden. Damit ist es möglich das Verfahren und Methoden für Weizen- und Möhrenproben zu charakterisieren. Es wurden verschiedene Einflussgrößen untersucht. Das Verfahren ist besonders gegenüber Änderungen in der Probenvorbereitung (z.B. Vermahlung, Mischungsverhältnis) empfindlich. Es wurde sowohl die Methodenstreuung, als auch der Anteil einzelner Schritte an der Gesamtstreuung für Weizen-, Möhren- und Apfelproben ermittelt. Die Verdampfung und Kristallisation hat den größten Anteil an der Gesamtstreuung. Die Durchführung eines Laboreignungstests zeigte, dass die so dokumentierten und charakterisierten Methoden in anderen Laboratorien erfolgreich eingesetzt werden können. Das Verfahren wurde für die nominale Unterscheidung von Weizen-, Möhren- und Apfelproben aus unterschiedlichem Anbau und Verarbeitungsschritten eingesetzt. Weizen-, Möhren- und Apfelproben aus definiertem Anbau können signifikant unterschieden werden. Weizen-, Möhren- und Apfelproben vom Erzeuger (Markt) konnten im Paarvergleich (ökologisch, konventionell) teilweise signifikant getrennt werden. Das Verfahren ist auch für die Charakterisierung von verarbeiteten Proben einsetzbar. Es konnte der Einfluss von Saftherstellung, Erwärmung und Alterung signifikant gezeigt werden. Darüber hinaus lässt sich das Verfahren auf weitere Probenarten anwenden. Das Verfahren arbeitet ganzheitlich, d.h. es werden keine Einzelstoffe analytisch bestimmt, sondern als Ergebnis wird ein Bild erhalten. Die Textur- und Struktureigenschaften dieses Bildes können mit standardisierten Methoden ausgewertet werden.

Estimating the motion of an underwater robot from a monocular image sequence

When underwater vehicles perform navigation close to the ocean floor, computer vision techniques can be applied to obtain quite accurate motion estimates. The most crucial step in the vision-based estimation of the vehicle motion consists on detecting matchings between image pairs. Here we propose the extensive use of texture analysis as a tool to ameliorate the correspondence problem in underwater images. Once a robust set of correspondences has been found, the three-dimensional motion of the vehicle can be computed with respect to the bed of the sea. Finally, motion estimates allow the construction of a map that could aid to the navigation of the robot

A proposal to estimate the motion of an underwater vehicle through visual mosaicking

This thesis proposes a solution to the problem of estimating the motion of an Unmanned Underwater Vehicle (UUV). Our approach is based on the integration of the incremental measurements which are provided by a vision system. When the vehicle is close to the underwater terrain, it constructs a visual map (so called "mosaic") of the area where the mission takes place while, at the same time, it localizes itself on this map, following the Concurrent Mapping and Localization strategy. The proposed methodology to achieve this goal is based on a feature-based mosaicking algorithm. A down-looking camera is attached to the underwater vehicle. As the vehicle moves, a sequence of images of the sea-floor is acquired by the camera. For every image of the sequence, a set of characteristic features is detected by means of a corner detector. Then, their correspondences are found in the next image of the sequence. Solving the correspondence problem in an accurate and reliable way is a difficult task in computer vision. We consider different alternatives to solve this problem by introducing a detailed analysis of the textural characteristics of the image. This is done in two phases: first comparing different texture operators individually, and next selecting those that best characterize the point/matching pair and using them together to obtain a more robust characterization. Various alternatives are also studied to merge the information provided by the individual texture operators. Finally, the best approach in terms of robustness and efficiency is proposed. After the correspondences have been solved, for every pair of consecutive images we obtain a list of image features in the first image and their matchings in the next frame. Our aim is now to recover the apparent motion of the camera from these features. Although an accurate texture analysis is devoted to the matching pro-cedure, some false matches (known as outliers) could still appear among the right correspon-dences. For this reason, a robust estimation technique is used to estimate the planar transformation (homography) which explains the dominant motion of the image. Next, this homography is used to warp the processed image to the common mosaic frame, constructing a composite image formed by every frame of the sequence. With the aim of estimating the position of the vehicle as the mosaic is being constructed, the 3D motion of the vehicle can be computed from the measurements obtained by a sonar altimeter and the incremental motion computed from the homography. Unfortunately, as the mosaic increases in size, image local alignment errors increase the inaccuracies associated to the position of the vehicle. Occasionally, the trajectory described by the vehicle may cross over itself. In this situation new information is available, and the system can readjust the position estimates. Our proposal consists not only in localizing the vehicle, but also in readjusting the trajectory described by the vehicle when crossover information is obtained. This is achieved by implementing an Augmented State Kalman Filter (ASKF). Kalman filtering appears as an adequate framework to deal with position estimates and their associated covariances. Finally, some experimental results are shown. A laboratory setup has been used to analyze and evaluate the accuracy of the mosaicking system. This setup enables a quantitative measurement of the accumulated errors of the mosaics created in the lab. Then, the results obtained from real sea trials using the URIS underwater vehicle are shown.

Layer-by-layer coating of alginate matrices with chitosan–alginate for the improved survival and targeted delivery of probiotic bacteria after oral administration

The oral administration of probiotic bacteria has shown potential in clinical trials for the alleviation of specific disorders of the gastrointestinal tract. However, cells must be alive in order to exert these benefits. The low pH of the stomach can greatly reduce the number of viable microorganisms that reach the intestine, thereby reducing the efficacy of the administration. Herein, a model probiotic, Bifidobacterium breve, has been encapsulated into an alginate matrix before coating in multilayers of alternating alginate and chitosan. The intention of this formulation was to improve the survival of B. breve during exposure to low pH and to target the delivery of the cells to the intestine. The material properties were first characterized before in vitro testing. Biacore™ experiments allowed for the polymer interactions to be confirmed; additionally, the stability of these multilayers to buffers simulating the pH of the gastrointestinal tract was demonstrated. Texture analysis was used to monitor changes in the gel strength during preparation, showing a weakening of the matrices during coating as a result of calcium ion sequestration. The build-up of multilayers was confirmed by confocal laser-scanning microscopy, which also showed the increase in the thickness of coat over time. During exposure to in vitro gastric conditions, an increase in viability from <3 log(CFU) per mL, seen in free cells, up to a maximum of 8.84 ± 0.17 log(CFU) per mL was noted in a 3-layer coated matrix. Multilayer-coated alginate matrices also showed a targeting of delivery to the intestine, with a gradual release of their loads over 240 min.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Colorizing Grey Scale Images

The purpose of this thesis is to develop a working methodology to color a grey scale image. This thesis is based on approach of using a colored reference image. Coloring grey scale images has no exact solution till date and all available methods are based on approximation. This technique of using a color reference image for approximating color information in grey scale image is among most modern techniques.Method developed here in this paper is better than existing methods of approximation of color information addition in grey scale images in brightness, sharpness, color shade gradients and distribution of colors over objects.Color and grey scale images are analyzed for statistical and textural features. This analysis is done only on basis of luminance value in images. These features are then segmented and segments of color and grey scale images are mapped on basis of distances of segments from origin. Then chromatic values are transferred between these matched segments from color image to grey scale image.Technique proposed in this paper uses better mechanism of mapping clusters and mapping colors between segments, resulting in notable improvement in existing techniques in this category.

Adição de areia para dispersão de solos na análise granulométrica

A acurácia da análise granulométrica depende da obtenção de suspensões de solo completamente dispersas e estáveis para possibilitar a separação das suas frações granulométricas. O objetivo do presente trabalho foi avaliar a eficácia da adição de quantidades e tamanhos de grãos de areia na fase de dispersão da análise granulométrica de solos, visando à maior acurácia na obtenção dos resultados da análise granulométrica. Os solos utilizados foram: Latossolo Vermelho eutroférrico (LVef), LatossoloVermelho acriférrico (LVwf), Latossolo Vermelho eutrófico (LVe), Argissolo Vermelho-Amarelo eutrófico (PVAe) e Nitossolo Vermelho eutroférrico (NVef). A dispersão das amostras dos solos foi realizada por meio da adição de hidróxido de sódio e agitação rotativa (60 rpm) por 16 h. O delineamento experimental adotado foi o inteiramente casualizado, com esquema fatorial 6 x 2, com três repetições. Os tratamentos foram constituídos por seis quantidades (0, 5, 10, 15, 20 e 25 g) e dois diâmetros (2,0-1,0 e 1,0-0,5 mm) de areia, adicionados na fase de dispersão da análise granulométrica dos solos. de acordo com as equações ajustadas, a adição de areia com diâmetro entre 1,0 e 0,5 mm nas quantidades de 21,4 g para LVef, 19,6 g para LVwf e 25,8 g para NVef proporciona, respectivamente para esses solos, aumentos de 50, 38 e 14,5 % nos teores de argila. No LVe e no PVAe não se justifica a adição de areia na análise granulométrica, pois esses solos não apresentaram problemas de dispersão. Os resultados demonstram que a adição de 25 g de areia, com diâmetro entre 1,0 e 0,5 mm, na fase de dispersão da análise granulométrica de solos argilosos com altos teores de óxidos de Fe e com dificuldades de dispersão, é eficiente para promover efetiva dispersão das partículas primárias do solo.

Plant species identification using multi-scale fractal dimension applied to images of adaxial surface epidermis

This paper presents the study of computational methods applied to histological texture analysis in order to identify plant species, a very difficult task due to the great similarity among some species and presence of irregularities in a given species. Experiments were performed considering 300 ×300 texture windows extracted from adaxial surface epidermis from eight species. Different texture methods were evaluated using Linear Discriminant Analysis (LDA). Results showed that methods based on complexity analysis perform a better texture discrimination, so conducting to a more accurate identification of plant species. © 2009 Springer Berlin Heidelberg.

Desequilíbrio de ligação e associação entre polimorfismos de base única com maciez da carne e espessura de gordura em bovinos Nelore utilizando painéis de alta densidade

Pós-graduação em Genética e Melhoramento Animal - FCAV

Síntese e caracterização de híbridos inorgânico-orgânicos funcionalizados com tiouréia e 2,2’-dipiridilamina: aplicação em adsorção de íons metálicos, pré-concentração e catálise

Pós-graduação em Ciência dos Materiais - FEIS

Método de detecção e classificação de lesões de pele em imagens digitais a partir do modelo Chan-Vese e máquina de vetor de suporte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)