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Aerosol Jet® Printing (AJP) is a direct-write based additive manufacturing process that is capable of printing electronics with fine features and various materials. It eliminates the complex masking process in traditional semiconductor manufacturing, thus enables flexible electronics design and reduces manufacturing cost. However, the quality control of AJP processes is still a challenging problem, primarily due to the lack of understanding of the potential root causes of the quality issues. There is a complex interaction among process setting variables, in situ feature variables, and quality variables in AJP processes. In this research, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the in situ feature variables, and the effect of the in situ feature variables on quality variables in a two-level hierarchical way. By identifying significant in situ feature variables as responses for the process setting variables, as well as predictors for product quality in a joint estimation problem, the proposed models have a hierarchical variable relationship to enable in situ process control for variation reduction and defect mitigation. A real case study is investigated to demonstrate the advantages of the proposed method. / Master of Science / Printed electronics is a promising technique for the future of the electronics manufacturing industry due to its potential for producing thin, flexible and low cost electronic devices. For the printing of any electronic device, a fundamental step is to print the conductive wires. Aerosol Jet® Printing (AJP) is one of the emerging additive manufacturing technologies for printing the conductive wires on a variety of substrates. It is a maskless additive manufacturing technique capable of printing high resolution wires. However, the quality control of AJP processes is still a challenging problem, primarily due to the lack of understanding of the potential root cause factors of the quality issues. There is a complex interaction among process setting variables, <i>in situ</i> feature variables, and quality variables. More importantly, the selection of the <i>in situ</i> feature variables is typically based on engineering domain knowledge and sensor instrumentation capability, rather than based on statistical significance of variables. In this research, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the <i>in situ</i> feature variables, and the effect of the <i>in situ</i> feature variables on quality variables in a two-level hierarchical way. By identifying significant <i>in situ</i> feature variables as responses for the process setting variables, as well as predictors for product quality in a joint estimation problem, the proposed models have a hierarchical variable relationship to enable <i>in situ</i> process control for variation reduction and defect mitigation. A real case study is investigated to demonstrate the advantages of the proposed method.

Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images. For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A sufficient number of hypothetical ellipses are then generated for each nuclei contour. 3) Next, a set of representative training samples and discriminative features are selected by a two-stage sparse model. 4) A classifier is trained using the refined training data. 5) Final nuclei detection is obtained by mean-shift clustering based on inner distance. The proposed method was tested on a set of images containing over 1500 nuclei. The results outperform the current state-of-the-art approaches. For brain tumor histopathological images, the major challenges are to handle significant variations in cell appearance and to split touching cells. The proposed novel automatic cell detection consists of: 1) Sparse reconstruction for splitting touching cells. 2) Adaptive dictionary learning for handling cell appearance variations. The proposed method was extensively tested on a data set with over 2000 cells. The result outperforms other state-of-the-art algorithms with F1 score = 0.96.

This thesis deals with the segmentation of microscopic images from plant sections. It describes the importance of the histogram for obtaining information from the image, and the utilization of the wavelet transformation for the preprocessing of the images. The thesis describes and categorizes different segmentation methods. In the thesis we use MATLAB for the validation of the presented theories and as the interface for creating a software model. The created software application automatically analyzes and evaluates microscopic images.

Schistosoma mansoni é o parasita causador da esquistossomose mansônica que, de acordo com o Ministério da Saúde do Brasil, afeta atualmente vários milhões de pessoas no país. Uma das formas de diagnóstico da esquistossomose é a detecção de ovos do parasita através da análise de lâminas microscópicas com material fecal. Esta tarefa é extremamente cansativa, principalmente nos casos de baixa endemicidade, pois a quantidade de ovos é muito pequena. Nesses casos, uma abordagem computacional para auxílio na detecção de ovos facilitaria o trabalho de diagnóstico. Os ovos têm formato ovalado, possuem uma membrana translúcida, apresentam uma espícula e sua cor é ligeiramente amarelada. Porém nem todas essas características são observadas em todos os ovos e algumas delas são visíveis apenas com uma ampliação adequada. Além disso, o aspecto visual do material fecal varia muito de indivíduo para indivíduo em termos de cor e presença de diversos artefatos (tais como partículas que não são desintegradas pelo sistema digestivo), tornando difícil a tarefa de detecção dos ovos. Neste trabalho investigamos, em particular, o problema de detecção das linhas que contornam a borda de vários dos ovos. Propomos um método composto por duas fases. A primeira fase consiste na detecção de estruturas do tipo linha usando operadores morfológicos. A detecção de linhas é dividida em três etapas principais: (i) realce de linhas, (ii) detecção de linhas, e (iii) refinamento do resultado para eliminar segmentos de linhas que não são de interesse. O resultado dessa fase é um conjunto de segmentos de linhas. A segunda fase consiste na detecção de subconjuntos de segmentos de linha dispostos em formato elíptico, usando um algoritmo baseado na transformada Hough. As elipses detectadas são fortes candidatas a contorno de ovos de S. mansoni. Resultados experimentais mostram que a abordagem proposta pode ser útil para compor um sistema de auxílio à detecção dos ovos. / Schistosoma mansoni is one of the parasites which causes schistosomiasis. According to the Brazilian Ministry of Health, several million people in the country are currently affected by schistosomiasis. One way of diagnosing it is by egg identification in stool. This task is extremely time-consuming and tiring, especially in cases of low endemicity, when only few eggs are present. In such cases, a computational approach to help the detection of eggs would greatly facilitate the diagnostic task. Schistosome eggs present oval shape, have a translucent membrane and a spike, and their color is slightly yellowish. However, not all these features are observed in every egg and some of them are visible only with an adequate microscopic magnification. Furthermore, the visual aspect of the fecal material varies widely from person to person in terms of color and presence of different artifacts (such as particles which are not disintegrated by the digestive system), making it difficult to detect the eggs. In this work we investigate the problem of detecting lines which delimit the contour of the eggs. We propose a method comprising two steps. The first phase consists in detecting line-like structures using morphological operators. This line detection phase is divided into three steps: (i) line enhancement, (ii) line detection, and (iii) result refinement in order to eliminate line segments that are not of interest. The output of this phase is a set of line segments. The second phase consists in detecting subsets of line segments arranged in an elliptical shape, using an algorithm based on the Hough transform. Detected ellipses are strong candidates to contour of S. mansoni eggs. Experimental results show that the proposed approach has potential to be effectively used as a component in a computer system to help egg detection.

Schistosoma mansoni é o parasita causador da esquistossomose mansônica que, de acordo com o Ministério da Saúde do Brasil, afeta atualmente vários milhões de pessoas no país. Uma das formas de diagnóstico da esquistossomose é a detecção de ovos do parasita através da análise de lâminas microscópicas com material fecal. Esta tarefa é extremamente cansativa, principalmente nos casos de baixa endemicidade, pois a quantidade de ovos é muito pequena. Nesses casos, uma abordagem computacional para auxílio na detecção de ovos facilitaria o trabalho de diagnóstico. Os ovos têm formato ovalado, possuem uma membrana translúcida, apresentam uma espícula e sua cor é ligeiramente amarelada. Porém nem todas essas características são observadas em todos os ovos e algumas delas são visíveis apenas com uma ampliação adequada. Além disso, o aspecto visual do material fecal varia muito de indivíduo para indivíduo em termos de cor e presença de diversos artefatos (tais como partículas que não são desintegradas pelo sistema digestivo), tornando difícil a tarefa de detecção dos ovos. Neste trabalho investigamos, em particular, o problema de detecção das linhas que contornam a borda de vários dos ovos. Propomos um método composto por duas fases. A primeira fase consiste na detecção de estruturas do tipo linha usando operadores morfológicos. A detecção de linhas é dividida em três etapas principais: (i) realce de linhas, (ii) detecção de linhas, e (iii) refinamento do resultado para eliminar segmentos de linhas que não são de interesse. O resultado dessa fase é um conjunto de segmentos de linhas. A segunda fase consiste na detecção de subconjuntos de segmentos de linha dispostos em formato elíptico, usando um algoritmo baseado na transformada Hough. As elipses detectadas são fortes candidatas a contorno de ovos de S. mansoni. Resultados experimentais mostram que a abordagem proposta pode ser útil para compor um sistema de auxílio à detecção dos ovos. / Schistosoma mansoni is one of the parasites which causes schistosomiasis. According to the Brazilian Ministry of Health, several million people in the country are currently affected by schistosomiasis. One way of diagnosing it is by egg identification in stool. This task is extremely time-consuming and tiring, especially in cases of low endemicity, when only few eggs are present. In such cases, a computational approach to help the detection of eggs would greatly facilitate the diagnostic task. Schistosome eggs present oval shape, have a translucent membrane and a spike, and their color is slightly yellowish. However, not all these features are observed in every egg and some of them are visible only with an adequate microscopic magnification. Furthermore, the visual aspect of the fecal material varies widely from person to person in terms of color and presence of different artifacts (such as particles which are not disintegrated by the digestive system), making it difficult to detect the eggs. In this work we investigate the problem of detecting lines which delimit the contour of the eggs. We propose a method comprising two steps. The first phase consists in detecting line-like structures using morphological operators. This line detection phase is divided into three steps: (i) line enhancement, (ii) line detection, and (iii) result refinement in order to eliminate line segments that are not of interest. The output of this phase is a set of line segments. The second phase consists in detecting subsets of line segments arranged in an elliptical shape, using an algorithm based on the Hough transform. Detected ellipses are strong candidates to contour of S. mansoni eggs. Experimental results show that the proposed approach has potential to be effectively used as a component in a computer system to help egg detection.

Ce travail porte sur l'analyse d'images numériques biologiques. Il vise à définir et mettre en œuvre des processus de mesures automatiques de données biologiques à partir d'images numériques dans un cadre de traitement de masse, et aborde notamment : l'incidence des choix méthodologiques sur la stabilité des résultats, l'étude de la validation des mesures produites et les limites de la généricité des méthodes et modèles appliquées à la biologie végétale.La réflexion est menée dans le cadre de l'étude de certaines organisations cellulaires, et plus particulièrement de l'identification et l'analyse automatique de files cellulaires dans des mosaïques d'images microscopiques de bois. En effet, l'étude des tendances biologiques le long de ces structures est nécessaire pour comprendre la mise en place des différentes organisations et maturations de cellule. Elle ne peut être conduite qu'à partir d'une grande zone d'observation du plan ligneux. Pour cela,- nous avons mis en place un nouveau protocole de préparation (rondelles de bois poncées) et de numérisation des échantillons permettant d'acquérir entièrement la zone d'observation sans biais- nous avons développé une chaîne de traitement permettant l'extraction automatique des files cellulaires dans des mosaïques images numériques.- nous avons proposé des indices de fiabilité pour chaque mesure effectuée afin de mieux cibler les études statistiques à venir.Les méthodes développées dans la thèse permettent l'acquisition et le traitement rapide d'un volume important de données. Ces données devraient servir de base à de nombreuses investigations : des analyses architecturales des arbres avec le suivi de file cellulaire et/ou la détection de perturbations biologiques, des analyses de variabilité intra et inter arbres permettant de mieux comprendre la croissance endogène des arbres. / This study focuses on biological numeric picture processes. It aims to define and implement new automated measurements at large scale analysis. Moreover, this thesis addresses: The incidence of the proposed methodology on the results reliability measurements accuracy definition and analysis proposed approaches reproducibility limits when applied to plant biology.This work is part of cells organization study, and aims to automatically identify and analyze the cell lines in microscopic mosaic wood slice pictures. Indeed, the study of biological tendencies among the cells lines is necessary to understand the cell migration and organization. Such a study can only be realized from a huge zone of observation of wood plane. To this end, this work proposes:- a new protocol of preparation (slices of sanded wood) and of digitizing of samples, in order to acquire the entire zone of observation without bias,- a novel processing chain that permit the automated cell lines extraction in numeric mosaic pictures,- reliability indexes for each measurement for further efficient statistical analysis.The methods developed during this thesis enable to acquire and treat rapidly an important volume of information. Those data define the basis of numerous investigations, such as tree architectural analysis cell lines following and/or detection of biological perturbations. And it finally helps the analysis of the variability intra- or inter- trees, in order to better understand the tree endogenous growth.