A computer-aided detection system for clustered microcalcifications.
Objective: The aim of this paper is to describe a novel system for computer-aided detection of clusters of microcalcifications on digital mammograms. Methods and Material: Mammograms are first segmented by means of a tree-structured Markov random field algorithm that extracts the elementary homogene...
| Publicado en: | Artificial Intelligence in Medicine Vol. 50; no. 1; pp. 23 - 33 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105078980&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105078980 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Sep2010 vid: 50 iid: 1 pid: 1004 pub: Elsevier B.V. artinfo: ui: 105078980 NLM20472412 2010748763 10.1016/j.artmed.2010.04.007 NLM20472412 105078980 ppf: 23 ppct: 10 formats: tig: atl: A computer-aided detection system for clustered microcalcifications. aug: au: Marrocco C Molinara M D'Elia C Tortorella F Marrocco, Claudio Molinara, Mario D'Elia, Ciro Tortorella, Francesco affil: Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell'Informazione e Matematica Industriale, Università degli Studi di Cassino, Via G. di Biasio 43, Cassino, FR, Italy sug: subj: Breast Diseases Radiography Calcinosis Radiography Cluster Analysis Decision Support Systems, Clinical Decision Support Techniques Mammography Medical Informatics Radiographic Image Interpretation, Computer-Assisted Algorithms Artificial Intelligence Data Mining Databases Female Human Probability Models, Statistical Netherlands Information Science Predictive Value of Tests Prognosis Female ab: Objective: The aim of this paper is to describe a novel system for computer-aided detection of clusters of microcalcifications on digital mammograms. Methods and Material: Mammograms are first segmented by means of a tree-structured Markov random field algorithm that extracts the elementary homogeneous regions of interest. An analysis of such regions is then performed by means of a two-stage, coarse-to-fine classification based on both heuristic rules and classifier combination. In this phase, we avoid taking a decision on the single microcalcifications and forward it to the successive phase of clustering realized through a sequential approach. Results: The system has been tested on a publicly available database of mammograms and compared with previous approaches. The obtained results show that the system is very effective, especially in terms of sensitivity. Conclusions: The proposed approach exhibits some remarkable advantages both in segmentation and classification phases. The segmentation phase employs an image model that reduces the computational burden, preserving the small details in the image through an adaptive local estimation of all model parameters. The classification stage combines the results of the classifiers focused on the single microcalcification and the cluster as a whole. Such an approach makes a detection system particularly effective and robust with respect to the large variations exhibited by the clusters of microcalcifications. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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