Microcalcification evaluation in computer assisted diagnosis for digital mammography

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dc.contributor.author Martí Bonmatí, Joan
dc.contributor.author Batlle i Grabulosa, Joan
dc.contributor.author Cufí i Solé, Xavier
dc.contributor.author Español, Josep
dc.date.issued 1999
dc.identifier.citation Martí, J., Batlle, J., Cufí, X., i Español, J. (1999). IEE Colloquium on Medical Applications of Signal Processing, 107, 7/1 - 7/6. Recuperat 06 maig 2010, a http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=828606
dc.identifier.uri http://hdl.handle.net/10256/2182
dc.description.abstract 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
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.relation.ispartof © IEE Colloquium on Medical Applications of Signal Processing, 1999, núm. 107, p. 7-7
dc.relation.ispartofseries Articles publicats (D-ATC)
dc.rights Tots els drets reservats
dc.subject Diagnòstic per la imatge
dc.subject Imatges -- Segmentació
dc.subject Imatges mèdiques
dc.subject Mama -- Radiografia
dc.subject Imaging segmentation
dc.subject Radiografia mèdica -- Tècniques digitals
dc.subject Breast -- Radiography
dc.subject Diagnostic imaging
dc.subject Imaging systems in medicine
dc.subject Radiography, Medical -- Digital techniques
dc.title Microcalcification evaluation in computer assisted diagnosis for digital mammography
dc.type info:eu-repo/semantics/article

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