Document Detail


Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines.
MedLine Citation:
PMID:  15894178     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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 Az=0.79 and 0.77 for the original and enhanced feature set, respectively, while for the MIAS dataset the corresponding characterization scores were Az=0.81 and 0.80. Utilizing neural network classification methodology, the corresponding performance for the Nijmegen dataset was Az=0.70 and 0.76 while for the MIAS dataset it was Az=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.
Authors:
A Papadopoulos; D I Fotiadis; A Likas
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Publication Detail:
Type:  Journal Article     Date:  2004-12-15
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  34     ISSN:  0933-3657     ISO Abbreviation:  Artif Intell Med     Publication Date:  2005 Jun 
Date Detail:
Created Date:  2005-05-16     Completed Date:  2005-09-13     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  141-50     Citation Subset:  IM    
Affiliation:
Department of Medical Physics, Medical School, University of Ioannina, GR 45110 Ioannina, Greece.
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MeSH Terms
Descriptor/Qualifier:
Breast Diseases / radiography*
Calcinosis / radiography*
Female
Humans
Mammography / statistics & numerical data*
Neural Networks (Computer)*
Radiographic Image Enhancement

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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