Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Computer aided diagnosis algorithms for digital microscopy

Automatic analysis and information extraction from an image is still a highly chal- lenging research problem in the computer vision area, attempting to describe the image content with computational and mathematical techniques. Moreover the in- formation extracted from the image should be meaningful and as most discrimi- natory as possible, since it will be used to categorize its content according to the analysed problem. In the Medical Imaging domain this issue is even more felt because many important decisions that affect the patient care, depend on the use- fulness of the information extracted from the image. Manage medical image is even more complicated not only due to the importance of the problem, but also because it needs a fair amount of prior medical knowledge to be able to represent with data the visual information to which pathologist refer. Today medical decisions that impact patient care rely on the results of laboratory tests to a greater extent than ever before, due to the marked expansion in the number and complexity of offered tests. These developments promise to improve the care of patients, but the more increase the number and complexity of the tests, the more increases the possibility to misapply and misinterpret the test themselves, leading to inappropriate diagnosis and therapies. Moreover, with the increased number of tests also the amount of data to be analysed increases, forcing pathologists to devote much time to the analysis of the tests themselves rather than to patient care and the prescription of the right therapy, especially considering that most of the tests performed are just check up tests and most of the analysed samples come from healthy patients. Then, a quantitative evaluation of medical images is really essential to overcome uncertainty and subjectivity, but also to greatly reduce the amount of data and the timing for the analysis. In the last few years, many computer assisted diagno- sis systems have been developed, attempting to mimic pathologists by extracting features from the images. Image analysis involves complex algorithms to identify and characterize cells or tissues using image pattern recognition technology. This thesis addresses the main problems associated to the digital microscopy analysis in histology and haematology diagnosis, with the development of algorithms for the extraction of useful information from different digital images, but able to distinguish different biological structures in the images themselves. The proposed methods not only aim to improve the degree of accuracy of the analysis, and reducing time, if used as the only means of diagnoses, but also they can be used as intermediate tools for skimming the number of samples to be analysed directly from the pathologist, or as double check systems to verify the correct results of the automated facilities used today

Augmented Reality Advertisement on Android as A New Marketing Strategy (ReADroid)

Augmented Reality Advertisement on Android as A New Marketing Stategy is a project to study the effectiveness of using augmented reality technology in marketing strategy based on the prototype being developed called Augmented Reality Advertisement on Android (ReADroid). ReADroid is an Android application for chocolate based advertisement which animate when ttre smartphone camera is pointed to the chocolate wrapper by using augmented reality (AR) approach. In the conventional physical advertising method which mostly dull and static, viewer has high tendency to ignore without gving any bodily responses towards the advertisement. This problem leads to viewer difficulties to remember the advertisement well. By creating and developing an interactive and engaging AR advertising application, ReADroid ensure that half of the advertisement viewers remember about the advertisement. As a little number of researches has been done to investigate the effectiveness of AR advertisement, this project is contributing to the evaluation on the usability and receptivity of users towards the AR advertisement. Men and women between 15 to 35 years old are the main target goup that will b€ using ReADroid on their Android smartphone to see the AR chocolate based advertisement on the chocolate wrapper. [n order to facilitate the requirement changes, ReADroid development is based on the modified version of Waterfdl Methodology Model called Sashimi Methodology Model. Based on the data gathering and analysis, by using Qualcomm Augmented Reality (QCAR) as the AR framework, together with unity 3D Pro, Blender and Android softrrare Development Kit (SDK), ReADroid satisfy more than 50 respondents' needs by allowing us€r to criticize or praise the advertised products directly at the advertisement, play the garnes provided, view the promotion or visiting the advertiser website. As conclusion, this interactive application will solve the ineffestiveness in conventional physical advertisement, thus creating bigger plaform for business to acquire more profit

Toward knowledge-based automatic 3D spatial topological modeling from LiDAR point clouds for urban areas

Le traitement d'un très grand nombre de données LiDAR demeure très coûteux et nécessite des approches de modélisation 3D automatisée. De plus, les nuages de points incomplets causés par l'occlusion et la densité ainsi que les incertitudes liées au traitement des données LiDAR compliquent la création automatique de modèles 3D enrichis sémantiquement. Ce travail de recherche vise à développer de nouvelles solutions pour la création automatique de modèles géométriques 3D complets avec des étiquettes sémantiques à partir de nuages de points incomplets. Un cadre intégrant la connaissance des objets à la modélisation 3D est proposé pour améliorer la complétude des modèles géométriques 3D en utilisant un raisonnement qualitatif basé sur les informations sémantiques des objets et de leurs composants, leurs relations géométriques et spatiales. De plus, nous visons à tirer parti de la connaissance qualitative des objets en reconnaissance automatique des objets et à la création de modèles géométriques 3D complets à partir de nuages de points incomplets. Pour atteindre cet objectif, plusieurs solutions sont proposées pour la segmentation automatique, l'identification des relations topologiques entre les composants de l'objet, la reconnaissance des caractéristiques et la création de modèles géométriques 3D complets. (1) Des solutions d'apprentissage automatique ont été proposées pour la segmentation sémantique automatique et la segmentation de type CAO afin de segmenter des objets aux structures complexes. (2) Nous avons proposé un algorithme pour identifier efficacement les relations topologiques entre les composants d'objet extraits des nuages de points afin d'assembler un modèle de Représentation Frontière. (3) L'intégration des connaissances sur les objets et la reconnaissance des caractéristiques a été développée pour inférer automatiquement les étiquettes sémantiques des objets et de leurs composants. Afin de traiter les informations incertitudes, une solution de raisonnement automatique incertain, basée sur des règles représentant la connaissance, a été développée pour reconnaître les composants du bâtiment à partir d'informations incertaines extraites des nuages de points. (4) Une méthode heuristique pour la création de modèles géométriques 3D complets a été conçue en utilisant les connaissances relatives aux bâtiments, les informations géométriques et topologiques des composants du bâtiment et les informations sémantiques obtenues à partir de la reconnaissance des caractéristiques. Enfin, le cadre proposé pour améliorer la modélisation 3D automatique à partir de nuages de points de zones urbaines a été validé par une étude de cas visant à créer un modèle de bâtiment 3D complet. L'expérimentation démontre que l'intégration des connaissances dans les étapes de la modélisation 3D est efficace pour créer un modèle de construction complet à partir de nuages de points incomplets.The processing of a very large set of LiDAR data is very costly and necessitates automatic 3D modeling approaches. In addition, incomplete point clouds caused by occlusion and uneven density and the uncertainties in the processing of LiDAR data make it difficult to automatic creation of semantically enriched 3D models. This research work aims at developing new solutions for the automatic creation of complete 3D geometric models with semantic labels from incomplete point clouds. A framework integrating knowledge about objects in urban scenes into 3D modeling is proposed for improving the completeness of 3D geometric models using qualitative reasoning based on semantic information of objects and their components, their geometric and spatial relations. Moreover, we aim at taking advantage of the qualitative knowledge of objects in automatic feature recognition and further in the creation of complete 3D geometric models from incomplete point clouds. To achieve this goal, several algorithms are proposed for automatic segmentation, the identification of the topological relations between object components, feature recognition and the creation of complete 3D geometric models. (1) Machine learning solutions have been proposed for automatic semantic segmentation and CAD-like segmentation to segment objects with complex structures. (2) We proposed an algorithm to efficiently identify topological relationships between object components extracted from point clouds to assemble a Boundary Representation model. (3) The integration of object knowledge and feature recognition has been developed to automatically obtain semantic labels of objects and their components. In order to deal with uncertain information, a rule-based automatic uncertain reasoning solution was developed to recognize building components from uncertain information extracted from point clouds. (4) A heuristic method for creating complete 3D geometric models was designed using building knowledge, geometric and topological relations of building components, and semantic information obtained from feature recognition. Finally, the proposed framework for improving automatic 3D modeling from point clouds of urban areas has been validated by a case study aimed at creating a complete 3D building model. Experiments demonstrate that the integration of knowledge into the steps of 3D modeling is effective in creating a complete building model from incomplete point clouds

Texture and bubble size measurements for modelling concentrate grade in flotation froth systems

Includes bibliographical references (p. 237-244).Numerous, machine vision systems for froth flotation have been developed over the last ten years; however, there are many aspects of the systems, that still require further development before they become one of the standard instruments present on industrial flotation operations. This thesis aims to address these problems by developing improved measurement techniques and showing how these measurements can be used to model the concentrate grad e of the flotation cell being monitored in a manner which is, directly usable by plant personnel. This thesis, presents an improvement to the watershed algorithm for the measurement of bubble sixe distribution in flotation froths. Unlike the standard watershed algorithm, it is able to measure accurate bubble size distributions when both large and tiny bubbles are present in a flotation froth image. Flotation froths with “dynamic bubble size distribution s” are introduced and methods of reducing the high dimensional bubble size distribution data associated with them are discussed. A method of using characteristic histograms of frequently occurring bubble size distributions is introduced and shown to be an appropriate method to use. A number of standard texture measures are best suited to the classification of flotation froth images. Results show that the Fourier ring and texture spectrum based features, perform well whilst having a relatively small computational cost for classifying new images. Video footage from selected industrial operations has been used for the development of improved algorithms for the measurement of froth surface descriptors. Analyses of the relationship, between froth velocity, bubble size, froth class and concentrate grade are made. The results show that it possible to use a unified approach to model the concentrate grade, irrespective of the site on which the measurements are made. Results from three industrial case studies show that bubble size and texture measures can be used to identify froth classes. Furthermore the combination of froth classes and froth velocity information is shown to consistently account for the most variation in the data when the concentrate grade is modelled using a linear combination of these two measurements

GVSU Undergraduate and Graduate Catalog, 2007-2008

Grand Valley State University 2007-2008 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1082/thumbnail.jp

GVSU Undergraduate and Graduate Catalog, 2016-2017

Grand Valley State University 2016-2017 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1091/thumbnail.jp

GVSU Undergraduate and Graduate Catalog, 2016-2017

Grand Valley State University 2016-2017 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1091/thumbnail.jp

GVSU Undergraduate and Graduate Catalog, 2019-2020

Grand Valley State University 2019-2020 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1094/thumbnail.jp

GVSU Undergraduate and Graduate Catalog, 2014-2015

Grand Valley State University 2014-2015 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1089/thumbnail.jp