Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Artificial convolution neural network for medical image pattern recognition
Neural Networks - Special issue: automatic target recognition
Neural networks for pattern recognition
Neural networks for pattern recognition
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
Digital Image Processing
IEEE Transactions on Image Processing
Artificial Intelligence in Medicine
Computers in Biology and Medicine
Integrated Computer-Aided Engineering
Artificial Intelligence in Medicine
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Automatic microcalcification and cluster detection for digital and digitised mammograms
Knowledge-Based Systems
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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 sub-systems: 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.