A neural network based technique to locate and classify microcalcifications in digital mammograms

This paper proposes a technique that extracts suspicious areas containing microcalcifications in digital mammograms and classifies them into two categories whether they contain benign or malignant clusters. The centroids and radiuses provided by expert radiologist are being used to locate and extract suspicious areas. Neural network's generalisation abilities are used to classify them into benign or malignant. The technique has been implemented in C++ on the SP2 supercomputer. The database from the Department of Radiology at the University of Nijmegen and Lawrence Livermore National Laboratory has been used for the experiments. The preliminary results are very promising. Some of them are presented in this paper.

[1]  David J. Marchette,et al.  The detection of micro-calcifications in mammographic images using high dimensional features , 1994, Proceedings of IEEE Symposium on Computer-Based Medical Systems (CBMS).

[2]  Brijesh Verma,et al.  Fast training of multilayer perceptrons , 1997, IEEE Trans. Neural Networks.

[3]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms: applications of artificial neural networks. , 1992, Medical physics.

[4]  Carey E. Priebe,et al.  COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY , 1993 .

[5]  Baoyu Zheng,et al.  Multistage neural network for pattern recognition in mammogram screening , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[6]  K Doi,et al.  Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. , 1994, Medical physics.

[7]  Nico Karssemeijer,et al.  Adaptive Noise Equalization and Image Analysis in Mammography , 1993, IPMI.

[8]  Carey E. Priebe,et al.  Comparative evaluation of pattern recognition techniques for detection of microcalcifications , 1993, Electronic Imaging.

[9]  G. W. Rogers,et al.  The application of fractal analysis to mammographic tissue classification. , 1994, Cancer letters.

[10]  David R. Dance,et al.  Digital mammography: Image analysis and automatic classification of calcifications in ductal carcinoma in situ , 1994 .

[11]  Brijesh Verma.  Recognition of Rotating Images Using an Automatic Feature Extraction Technique and Neural Networks , 1997, Int. J. Neural Syst..