Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines
| dc.contributor.author | Papadopoulos, A. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:32:29Z | |
| dc.date.available | 2015-11-24T17:32:29Z | |
| dc.identifier.issn | 0933-3657 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13737 | |
| dc.rights | Default Licence | - |
| dc.subject | support vector machine | en |
| dc.subject | microcalcification cluster classification | en |
| dc.subject | mammography | en |
| dc.subject | computer-aided diagnosis | en |
| dc.subject | digital mammograms | en |
| dc.subject | breast-cancer | en |
| dc.subject | classification | en |
| dc.subject | segmentation | en |
| dc.subject | system | en |
| dc.title | Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines | en |
| heal.abstract | Objective : Detection and characterization of microcalcification clusters in mammograms is vital in daily clinical practice. The scope of this work is to present a novel computer-based automated method for the characterization of microcalcification clusters in digitized mammograms. Methods and material : The proposed method has been implemented in three stages: (a) the cluster detection stage to identify clusters of microcalcifications, (b) the feature extraction stage to compute the important features of each cluster and (c) the classification stage, which provides with the final characterization. In the classification stage, a rule-based system, an artificial neural network (ANN) and a support vector machine (SVM) have been implemented and evaluated using receiver operating characteristic (ROC) analysis. The proposed method was evaluated using the Nijmegen and Mammographic Image Analysis Society (MIAS) mammographic databases. The original feature set was enhanced by the addition of four rule-based features. Results and conclusions : In the case of Nijmegen dataset, the performance of the SVM was A(z) = 0.79 and 0.77 for the original and enhanced feature set, respectively, white for the MIAS dataset the corresponding characterization scores were A(z) = 0.81 and 0.80. Utilizing neural network classification methodology, the corresponding performance for the Nijmegen dataset was A(z) = 0.70 and 0.76 while for the MIAS dataset it was A(z) = 0.73 and 0.78. Although the obtained high classification performance can be successfully applied to microcalcification clusters characterization, further studies must be carried out for the clinical evaluation of the system using larger datasets. The use of additional features originating either from the image itself (such as cluster Location and orientation) or from the patient data may further improve the diagnostic value of the system. © 2004 Elsevier B.V. All rights reserved. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | DOI 10.1016/j.artmed.2004.10.001 | - |
| heal.identifier.secondary | <Go to ISI>://000229559200004 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S093336570400154X/1-s2.0-S093336570400154X-main.pdf?_tid=c25df36e3dd5fb2832267316aa5cb808&acdnat=1339758328_75cee38b523af04487185fed0e6d4eb8 | - |
| heal.journalName | Artif Intell Med | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2005 | - |
| heal.publisher | Elsevier | en |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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