Journal Article10.1142/S0129183100000808
System for automatic detection of clustered microcalcifications in digital mammograms
Armando Bazzani,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,D. Romani,Alessandro Bevilacqua +6 more
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TL;DR: This paper investigates the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms with a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.
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Abstract: In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.
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Citations
An SVM classifier to separate false signals from microcalcifications in digital mammograms
Armando Bazzani,Alessandro Bevilacqua,Dante Bollini,Rosa Brancaccio,Renato Campanini,Nico Lanconelli,A. Riccardi,D. Romani +7 more
TL;DR: This paper compares the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of the detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features.
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An automatic microcalcification detection system based on a hybrid neural network classifier
TL;DR: A hybrid intelligent system is presented for the identification of microcalcification clusters in digital mammograms using an intelligent system containing two sub-systems: a rule-based and a neural network sub- system.
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Computer aided diagnosis with case-based reasoning and genetic algorithms
TL;DR: A back-end phase is introduced, based on machine learning techniques, in a previous computer aided diagnosis system for breast cancer diagnosis using mammographic images, using case-based reasoning and genetic algorithms.
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Testing the performances of different image representations for mass classification in digital mammograms
Enrico Angelini,Renato Campanini,Emiro Iampieri,Nico Lanconelli,Matteo Masotti,Matteo Roffilli +5 more
TL;DR: A mass detection algorithm which does not refer explicitly to shape, border, size, contrast or texture of mammographic suspicious regions is evaluated and the improvement in the Az value with the pixel image representation is statistically significant compared to that obtained with the discrete wavelet and overcomplete wavelet image representations.
A Distributed Genetic Algorithm for Parameters Optimization to Detect Microcalcifications in Digital Mammograms
Alessandro Bevilacqua,Renato Campanini,Nico Lanconelli +2 more
- 18 Apr 2001
TL;DR: This paper sets up a method for the detection of clustered microcalcifications in digital mammograms, based on statistical techniques and multiresolution analysis by means of wavelet transform, and finds out parameters not influencing performance at all.
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References
Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography
TL;DR: The potential application of such a computer-aided system to mammographic interpretation is demonstrated by its ability to detect microcalcifications in clinical mammograms.
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Evaluating the performance of detection algorithms in digital mammography.
TL;DR: It is shown in this work that new performance indices are needed to fully describe the degree of detection and the type of detection (single calcification, cluster of calcifications, mass, or artifact).
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An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms
TL;DR: The wavelet transform approach can improve the detection of subtle clustered microcalcifications in mammograms and was useful in detecting some of the subtle microCalcifications that were not detected by the previous scheme, which was based on the difference-image technique.
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Automated detection of clustered microcalcifications in digital mammograms using wavelet processing techniques
Hiroyuki Yoshida,Kunio Doi,Robert M. Nishikawa +2 more
- 11 May 1994
TL;DR: An approach using the wavelet transform is employed to increase the signal-to-noise ratio of microcalcifications by suppressing the background structure of the breast image in order to increased the sensitivity and/or to reduce the false-positive rate.
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Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms
TL;DR: A technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of the CAD scheme based on the supervised learning method is developed.
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