The nature of statistical learning theory
The nature of statistical learning theory
Neural networks for pattern recognition
Neural networks for pattern recognition
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
An automatic microcalcification detection system based on a hybrid neural network classifier
Artificial Intelligence in Medicine
Integrated Computer-Aided Engineering
Proceedings of the International Conference and Workshop on Emerging Trends in Technology
A computer-aided detection system for clustered microcalcifications
Artificial Intelligence in Medicine
Computers & Mathematics with Applications
Effective recognition of MCCs in mammograms using an improved neural classifier
Engineering Applications of Artificial Intelligence
A graph-based method for detecting and classifying clusters in mammographic images
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Computerized classification can reduce unnecessary biopsies in BI-RADS category 4a lesions
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging
Knowledge-Based Systems
3D human action recognition using model segmentation
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Twin support vector machines and subspace learning methods for microcalcification clusters detection
Engineering Applications of Artificial Intelligence
International Journal of Computational Science and Engineering
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Classification of microcalcification clusters based on morphological topology analysis
IWDM'12 Proceedings of the 11th international conference on Breast Imaging
An efficient digital mammogram image classification using DTCWT and SVM
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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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, while 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.