Automatic Generation of Integration and Preprocessing Ontologies for Biomedical Sources in a Distributed Scenario

Access to a large number of remote data sources has boosted research in biomedicine, where different biological and clinical research projects are based on collaborative efforts among international organizations. In this scenario, the authors have developed various methods and tools in the area of database integration, using an ontological approach. This paper describes a method to automatically generate preprocessing structures (ontologies) within an ontology-based KDD model. These ontologies are obtained from the analysis of data sources, searching for: (i) valid numerical ranges (using clustering techniques), (ii) different scales, (iii) synonym transformations based on known dictionaries and (iv)typographical errors. To test the method, experiments were carried out with four biomedical databases―containing rheumatoid arthritis, gene expression patterns, biological processes and breast cancer patients― proving the performance of the approach. This method supports experts in data analysis processes, facilitating the detection of inconsistencies

Synergy between medical informatics and bioinformatics: facilitating genomic medicine for future health care

Medical Informatics (MI) and Bioinformatics (BI) are two interdisciplinary areas located at the intersection between computer science and medicine and biology, respectively. Historically, they have been separated and only occasionally have researchers of both disciplines collaborated. The completion of the Human Genome Project has brought about in this post genomic era the need for a synergy of these two disciplines to further advance in the study of diseases by correlating essential genotypic information with expressed phenotypic information. Biomedical Informatics (BMI) is the emerging technology that aims to put these two worlds together in the new rising genomic medicine. In this regard, institutions such as the European Commission have recently launched several initiatives to support a new combined research agenda, based on the potential for synergism of both disciplines. In this paper we review the results the BIOINFOMED study one of these projects funded by the E

Methods of Classification of the Genera and Species of Bacteria Using Decision Tree, Journal of Telecommunications and Information Technology, 2019, nr 4

This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristic

Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques

With the advances in the computer field, methods and techniques in automatic image processing and analysis provide the opportunity to detect automatically the change and degeneration in retinal images. Localization of the optic disc is extremely important for determining the hard exudate lesions or neovascularization, which is the later phase of diabetic retinopathy, in computer aided eye disease diagnosis systems. Whereas optic disc detection is fairly an easy process in normal retinal images, detecting this region in the retinal image which is diabetic retinopathy disease may be difficult. Sometimes information related to optic disc and hard exudate information may be the same in terms of machine learning. We presented a novel approach for efficient and accurate localization of optic disc in retinal images having noise and other lesions. This approach is comprised of five main steps which are image processing, keypoint extraction, texture analysis, visual dictionary, and classifier techniques. We tested our proposed technique on 3 public datasets and obtained quantitative results. Experimental results show that an average optic disc detection accuracy of 94.38%, 95.00%, and 90.00% is achieved, respectively, on the following public datasets: DIARETDB1, DRIVE, and ROC

Visualisierung zweidimensionaler Volumen

In dieser Arbeit wird ein neues Verfahren zur Visualisierung zweidimensionaler Volumen vorgestellt. Der Begriff multidimensionales Volumen wird dabei definiert als eine Menge von räumlich dreidimensionalen Datensätzen, die jeder eine andere Eigenschaft (eine physikalische Qualität, z.B. Dichte oder Temperatur) desselben Objekts beschreiben. Zweidimensionale Volumen beschreiben also zwei verschiedene Eigenschaften eines Objekts. Sie entstehen z.B. in biomedizinischen Anwendungen, wenn gleichzeitig funktionale und anatomische Datensätze untersucht werden. Zunächst wird der Stand der Technik in der Visualisierung zweidimensionaler Volumen dargelegt. Dabei sind besonders die folgenden Schwächen bestehender Verfahren erkennbar: - Schlechte räumliche Darstellung und schlechte Lokalisierbarkeit von Ausprägungen (bemerkenswerte Quantitäten einer Eigenschaft an einer Stelle). Beschränkung auf Datensätze aus speziellen Quellen oder spezielle Kombinationen von Datensätzen. - Prinzipbedingte Beschränkung einer Eigenschaft auf wenige kleine Regionen innerhalb der anderen Eigenschaft. Basierend auf diesen Defiziten werden die Anforderungen für ein besseres Visualisierungsverfahren herausgearbeitet, anhand derer ein neues Verfahren, dependent rendering genannt, entwickelt wird. Das Verfahren basiert auf der Annahme, dass bei der Visualisierung mehrerer Eigenschaften immer eine Eigenschaft als Referenz zur Lokalisierung dienen kann. Abhängig von der ersten kann eine weitere Eigenschaft visualisiert werden. Es werden drei Implementierungen des Verfahrens vorgestellt, die ersten beiden sind Prototypen, die dritte eine spezialisierte Anwendung für eine biomedizinische Visualisierungsplattform. Die Implementierungen veranschaulichen, dass sich das vorgestellte Verfahren gegenüber bestehenden Ansätzen besonders durch folgende Punkte auszeichnet: - Gute Lokalisierbarkeit von Ausprägungen bei gleichzeitiger guter räumlicher Darstellung des Objekts (z.B.: "Ist es auf der Oberfläche heiss oder innerhalb des Objekts?"). - Gleiche räumliche Ausdehnung beider Datensätze möglich. - Genereller Ansatz: Keine Beschränkung auf Datensätze aus speziellen Quellen oder auf spezielle Kombinationen von Datensätzen. Das vorgestellte Verfahren stellt daher einen bedeutenden Fortschritt in der Technik der Visualisierung zweier Eigenschaften eines Objekts dar.In this thesis, a new technique for the visualisation of two-dimensional volumes is presented. The term multi-dimensional volume is defined as a set of spatially three-dimensional data sets, each of them describing another property (a physical quality, e.g. density or temperature) of the same object. Thus, two-dimensional volumes describe two different properties of an object. They are used e.g. in biomedical imaging, where anatomical and functional data are examined jointly. First, the state of the art in the visualisation of two-dimensional volumes is presented. In the course of this, the following deficiencies of existing approaches become apparent: - Unsatisfactory 3D impression (it is difficult to mentally reconstruct the spatially three-dimensional object from the rendering) and difficult localisation of features (i.e. remarkable characteristics in the quantity of a property at a given location). - Restriction to data sets from particular origins or particular combinations of data sets. - By design, one property is restricted to only a few small regions inside the other property. Starting from these deficiencies, the requirements for a visualisation technique that overcomes these limitations are elaborated. These are then used to develop a new technique, called dependent rendering, which is based on the assumption that, when visualising two properties of an object, there is alway one property that can serve as a spatial reference for the other. The other property is then visualised in dependency on this reference. Three implementations of the technique are presented, the first two are prototypes, the third one is a specialised application for a biomedical visualisation platform. The implementations show that, compared to existing approaches, the presented technique especially stands out because of the following features: - Precise localisation of features combined with good 3D impression of the object (e.g. "Is it hot on the surface or only inside the object?"). - Both data sets can be extended over the same region. - General approach: No restriction to data sets from particular origins or particular combinations of data sets. The presented technique therefore represents an important advancement in the joint visualisation of two properties of an object

A review of feature-based retinal image analysis

Retinal imaging is a fundamental tool in ophthalmic diagnostics. The potential use of retinal imaging within screening programs, with consequent need to analyze large numbers of images with high throughput, is pushing the digital image analysis field to find new solutions for the extraction of specific information from the retinal image. The aim of this review is to explore the latest progress in image processing techniques able to recognize specific retinal image features. and potential features of disease. In particular, this review aims to describe publically available retinal image databases, highlight different performance evaluators commonly used within the field, outline current approaches in feature-based retinal image analysis, and to map related trends. This review found two key areas to be addressed for the future development of automatic retinal image analysis: fundus image quality and the affect image processing may impose on relevant clinical information within the images. Performance evaluators of the algorithms reviewed are very promising, however absolute values are difficult to interpret when validating system suitability for use within clinical practice

Desarrollo y utilización de métodos computacionales en la mejora del proceso de obtención de nuevos fármacos

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de lectura: 19-02-201