2 resultados para Benign entity

em Universitat de Girona, Spain


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gvSIG Mobile, la versión de gvSIG para dispositivos móviles presenta su nueva versión que incluye las esperadas funcionalidades de creación de nuevas entidades geográficas y utilización de formularios personalizados para edición de datos, además de nuevos formatos de datos vectoriales (GML, KML, GPX) y sistemas de referencia. Funcionalidades que se suman a las capacidades de visor de cartografía (ECW, SHP, WMS) y sistema de localización mediante GPS que ya posee. gvSIG Mobile está siendo desarrollado por Prodevelop, la Universitat de València e Iver para la Conselleria d’Infraestructures i Transport de la Generalitat Valenciana y se distribuye con una licencia GPL

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In order to develop applications for z;isual interpretation of medical images, the early detection and evaluation of microcalcifications in digital mammograms is verg important since their presence is often associated with a high incidence of breast cancers. Accurate classification into benign and malignant groups would help improve diagnostic sensitivity as well as reduce the number of unnecessa y biopsies. The challenge here is the selection of the useful features to distinguish benign from malignant micro calcifications. Our purpose in this work is to analyse a microcalcification evaluation method based on a set of shapebased features extracted from the digitised mammography. The segmentation of the microcalcifications is performed using a fixed-tolerance region growing method to extract boundaries of calcifications with manually selected seed pixels. Taking into account that shapes and sizes of clustered microcalcifications have been associated with a high risk of carcinoma based on digerent subjective measures, such as whether or not the calcifications are irregular, linear, vermiform, branched, rounded or ring like, our efforts were addressed to obtain a feature set related to the shape. The identification of the pammeters concerning the malignant character of the microcalcifications was performed on a set of 146 mammograms with their real diagnosis known in advance from biopsies. This allowed identifying the following shape-based parameters as the relevant ones: Number of clusters, Number of holes, Area, Feret elongation, Roughness, and Elongation. Further experiments on a set of 70 new mammogmms showed that the performance of the classification scheme is close to the mean performance of three expert radiologists, which allows to consider the proposed method for assisting the diagnosis and encourages to continue the investigation in the sense of adding new features not only related to the shape