Book Chapter10.1007/3-540-45723-2_46
Detection of Microcalcifications in Mammograms by the Combination of a Neural Detector and Multiscale Feature Enhancement
Diego Andina,A. Vega-Corona +1 more
- 13 Jun 2001
- pp 385-392
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TL;DR: A two steps method for the automatic classification of microcalcifications in Mammograms by the application of a Neural Network optimized in the Neyman-Pearson sense, which presents a controlled and very low probability of classifying abnormal images as normal.
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Abstract: We propose a two steps method for the automatic classification of microcalcifications in Mammograms. The first step performs the improvement of the visuaalization of any abnormal lesion through feature enhancement based in multiscale wavelet representations of the mammographic images. In a second step the automatic recognition of microcalcifications is achived by the application of a Neural Network optimized in the Neyman-Pearson sense. That means that the Neural Network presents a controlled and very low probability of classifying abnormal images as normal.
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Citations
Connectionist models of neurons, learning processes, and artificial intelligence
José Mira,Alberto Prieto +1 more
- 01 Jan 2001
TL;DR: Learning and Other Plasticity Phenomena, and Complex Systems Dynamics.
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Feature Vectors Generation for Detection of Microcalcifications in Digitized Mammography Using Neural Networks
A. Vega-Corona,Antonio Álvarez-Vellisco,Diego Andina +2 more
- 05 Jun 2009
TL;DR: A methodology that sinergically combines a select of successful advances in each step to automatically classify microcalcifications in digitized mammography and uses a Neural feature selector and detector to finally classify the MCs.
22
Advances in Neyman-Pearson Neural Detectors Design
Diego Andina,Santiago Torres-Alegre,A. Vega-Corona,Antonio Álvarez-Vellisco +3 more
- 05 Jun 2009
TL;DR: This chapter is dedicated to scope of the application of Importance Sampling Techniques to the design phase of Neyman-Pearson Neural Detectors.
3
•Dissertation
Aportación a la extracción de conocimiento aplicada a datos mediante agrupamientos y sistemas difusos
Benjamín Ojeda Magaña
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TL;DR: In this article, the authors propose a model of GKPFCM (Gustafson-Kessel Possibilistic Fuzzy c-Means) for the extraction of information and conocimiento.
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•Proceedings Article
Pollutant concentrations and Meteorological data classification by Neural Networks
A. Vega-Corona,J. M. Barrón-Adame,Oscar Ibarra-Manzano,M. G. Cortina-Januchs,J. Quintanilla-Dominguez,Diego Andina +5 more
- 24 Jun 2012
TL;DR: In this paper, an environmental contingency forecasting tool based on Neural Networks (NN) is presented, which analyzes every hour and daily Sulphur Dioxide (SO 2 ) concentrations and Meteorological data time series.
References
•Book
Characterization of Signals From Multiscale Edges
Stéphane Mallat,S. Zhong +1 more
- 11 Aug 2011
TL;DR: The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges and shows that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures.
Mixture densities, maximum likelihood, and the EM algorithm
TL;DR: This work discusses the formulation and theoretical and practical properties of the EM algorithm, a specialization to the mixture density context of a general algorithm used to approximate maximum-likelihood estimates for incomplete data problems.
3K
Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
TL;DR: It is concluded that three-layer, feed-forward neural networks with a back-propagation algorithm trained for the interpretation of mammograms on the basis of features extracted from mammograms by experienced radiologists may provide a potentially useful tool in the mammographic decision-making task of distinguishing between benign and malignant lesions.
479
Wavelet transforms for detecting microcalcifications in mammograms
R.N. Strickland,Hee Il Hahn +1 more
TL;DR: A 2-stage method based on wavelet transforms for detecting and segmenting calcifications designed to overcome the limitations of the simplistic Gaussian assumption and provides an accurate segmentation of calcification boundaries is developed.
393
Tree-structured nonlinear filters in digital mammography
TL;DR: In all applications, the proposed filter suggested better detail preservation, noise suppression, and edge detection than all other approaches and it may prove to be a useful tool for computer-assisted diagnosis in digital mammography.
134