Journal ArticleDOI
A hybrid system for detecting masses in mammographic images
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The hybrid system for detecting masses in mammographic images is discussed, which uses texture features, decision trees, and a multiresolution Markov random field model to analyze mammograms.Abstract:
This paper discusses the hybrid system for detecting masses in mammographic images. The proposed approach analyzes mammograms in three major steps. First, a global segmentation method is applied to find regions of interest. This step uses texture features, decision trees, and a multiresolution Markov random field model. The second stage works on the output of the previous algorithm. Here, a combination of three different local segmentation methods is used, and then, some relevant features are extracted. Some of them refer to the shape of the object; others are texture parameters. The final decision is made using a linear combination of these featuresread more
Citations
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Journal ArticleDOI
A review of automatic mass detection and segmentation in mammographic images.
Arnau Oliver,Jordi Freixenet,Joan Martí,Elsa Pérez,Josep Pont,Erika R. E. Denton,Reyer Zwiggelaar +6 more
TL;DR: The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies.
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Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.
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Computer-Aided Breast Cancer Detection Using Mammograms: A Review
TL;DR: This review aims at providing an overview about recent advances and developments in the field of Computer-Aided Diagnosis (CAD) of breast cancer using mammograms, specifically focusing on the mathematical aspects of the same, aiming to act as a mathematical primer for intermediates and experts inThe field.
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Classification of benign and malignant masses based on Zernike moments
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Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features
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References
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Journal ArticleDOI
Comparison of the Performance of Screening Mammography, Physical Examination, and Breast US and Evaluation of Factors that Influence Them: An Analysis of 27,825 Patient Evaluations
TL;DR: Mammographic sensitivity for breast cancer declines significantly with increasing breast density and is independently higher in older women with dense breasts, which significantly increases detection of small cancers and depicts significantly more cancers and at smaller size and lower stage than does PE, which detects independently extremely few cancers.
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Markov Random Field Modeling in Computer Vision
TL;DR: This book presents a comprehensive study on the use of MRFs for solving computer vision problems, and covers the following parts essential to the subject: introduction to fundamental theories, formulations of MRF vision models, MRF parameter estimation, and optimization algorithms.
Journal ArticleDOI
Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
Berkman Sahiner,Heang Ping Chan,Nicholas Petrick,Datong Wei,Mark A. Helvie,Dorit D. Adler,Mitchell M. Goodsitt +6 more
TL;DR: The authors' results demonstrate the feasibility of using a convolution neural network for classification of masses and normal tissue on mammograms using a generalized, fast and stable implementation of the CNN.
Journal ArticleDOI
Detection of stellate distortions in mammograms
Nico Karssemeijer,G.M. te Brake +1 more
TL;DR: A method is described to detect stellate patterns in mammograms based on statistical analysis of a map of pixel orientations, which provides an estimate of the orientation of this structure, whereas in other cases the image noise will generate a random orientation.
Journal ArticleDOI
Markov random field for tumor detection in digital mammography
TL;DR: The algorithm was notably successful in the detection of minimal cancers manifested by masses, and an extensive study of the effects of the algorithm's parameters on its sensitivity and specificity was performed in order to optimize the method for a clinical, observer performance study.