Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.

Resolución 437(XIX): Acceso a las fuentes de información = Resolution 437(XIX): Access to sources of information = Résolution 437(XIX): Accès aux sources d'information

Publicado separadamete en cada idioma

Proposed statutes of the Caribbean Congress of Information Professionals

Includes bibliography

Description of information activities of the Caribbean Documentation Centre

Analiza la información utilizada y generada por las unidades de información bibliográfica y los bancos de datos del Centro de Documentación del Caribe; su papel como coordinador regional del CARISPLAN; y presenta las actividades y una lista de publicaciones producidas por la Unidad de Población.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Selección de información para la toma de decisiones en el sector transporte = The choice of information for decision making in the transport sector

Incluye Bibliografía

An experience of information analysis development: INFOPLAN

Incluye Bibliografía

Clasificación de propuestas de proyectos mineros por países = Classification of mineral project proposals by countries

Complementa la información contenida en el documento E/CEPAL/G.1207

Draft amendments to the standard external trade statistics classification of East Caribbean Common Market Countries

Includes bibliography