An automatic microcalcification detection system based on a hybrid neural network classifier
| dc.contributor.author | Papadopoulos, A. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.contributor.author | Likas, A. | en |
| dc.date.accessioned | 2015-11-24T17:31:37Z | |
| dc.date.available | 2015-11-24T17:31:37Z | |
| dc.identifier.issn | 0933-3657 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13606 | |
| dc.rights | Default Licence | - |
| dc.subject | microcalcification detection | en |
| dc.subject | hybrid neural network | en |
| dc.subject | computer-aided detection (cad) | en |
| dc.subject | mammography | en |
| dc.subject | breast-cancer detection | en |
| dc.subject | digital mammograms | en |
| dc.subject | clustered microcalcifications | en |
| dc.subject | pattern-recognition | en |
| dc.subject | wavelet transform | en |
| dc.subject | death rates | en |
| dc.subject | segmentation | en |
| dc.subject | diagnosis | en |
| dc.subject | calcifications | en |
| dc.subject | enhancement | en |
| dc.title | An automatic microcalcification detection system based on a hybrid neural network classifier | en |
| heal.abstract | A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms. The proposed method is based on a three-step procedure: (a) preprocessing and segmentation, (b) regions of interest (ROI) specification, and (c) feature extraction and classification. The reduction of false positive cases is performed using an intelligent system containing two subsystems: a rule-based and a neural network sub-system. In the first step of the classification schema 22 features are automatically computed which refer either to individual microcalcifications or to groups of them. Further reduction in the number of features is achieved through principal component analysis (PCA). The proposed methodology is tested using the Nijmegen and the Mammographic Image Analysis Society (MIAS) mammographic databases. Results are presented as the receiver operating characteristic (ROC) performance and are quantified by the area under the ROC curve (A(z)). In particular, the A(z) value for the Nijmegen dataset is 0.91 and for the MIAS is 0.92. The detection specificity of the two sets is 1.80 and 1.15 false positive clusters per image, at the sensitivity level higher than 0.90, respectively. (C) 2002 Elsevier Science B.V. All rights reserved. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.secondary | <Go to ISI>://000176197900003 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0933365702000131/1-s2.0-S0933365702000131-main.pdf?_tid=081534f444f65602384a4cb65461fc9d&acdnat=1339758333_8c32202767a26e0488bcc67032178c7c | - |
| heal.journalName | Artif Intell Med | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2002 | - |
| 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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