Journal ArticleDOI
Noise equalization for detection of microcalcification clusters in direct digital mammogram images
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TLDR
It is shown that the square root model based approach which FFDM allows leads to a robust estimation of the high frequency image noise, which provides better microcalcification detection performance when compared to the film-screen noise equalization method developed by Veldkamp.Abstract:
Equalizing image noise is shown to be an important step in the automatic detection of microcalcifications in digital mammography. This study extends a well established film-screen noise equalization scheme developed by Veldkamp et al. for application to full-field digital mammogram (FFDM) images. A simple noise model is determined based on the assumption that quantum noise is dominant in direct digital X-ray imaging. Estimation of the noise as a function of the gray level is improved by calculating the noise statistics using a truncated distribution method. Experimental support for the quantum noise assumption is presented for a set of step wedge phantom images. Performance of the noise equalization technique is also tested as a preprocessing stage to a microcalcification detection scheme. It is shown that the square root model based approach which FFDM allows leads to a robust estimation of the high frequency image noise. This provides better microcalcification detection performance when compared to the film-screen noise equalization method developed by Veldkamp. Substantially better results are obtained than when noise equalization is omitted. A database of 124 direct digital mammogram images containing 28 microcalcification clusters was used for evaluation of the method.read more
Citations
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Journal ArticleDOI
Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances
TL;DR: An overview of recent advances in the development of CAD systems and related techniques for breast cancer detection and diagnosis focuses on key CAD techniques developed recently, including detection of calcifications, detection of masses, Detection of architectural distortion, detectionof bilateral asymmetry, image enhancement, and image retrieval.
Journal ArticleDOI
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.
Journal ArticleDOI
CADx of mammographic masses and clustered microcalcifications: a review.
Matthias Elter,Alexander Horsch +1 more
TL;DR: A comprehensive review of the state of the art of CADx approaches is presented, restricted to the two most important types of mammographic lesions: masses and clustered microcalcifications, and focuses on articles published in international journals.
Journal ArticleDOI
Relevance vector machine for automatic detection of clustered microcalcifications
TL;DR: This paper forms MC detection as a supervised-learning problem, and applies RVM as a classifier to determine at each location in the mammogram if an MC object is present or not, and develops computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms.
Journal ArticleDOI
Computer aided detection of clusters of microcalcifications on full field digital mammograms.
Jun Ge,Berkman Sahiner,Lubomir M. Hadjiiski,Heang Ping Chan,Jun Wei,Mark A. Helvie,Chuan Zhou +6 more
TL;DR: It was found that the computer-aided detection system to identify microcalcification clusters automatically on full field digital mammograms can achieve a cluster-based sensitivity of 70, 80, and 90 % and the CNN in combination with an LDA classifier could substantially reduce FPs with a small tradeoff in sensitivity.
References
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Book
Cluster Analysis
TL;DR: This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering.
Journal ArticleDOI
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.
Journal ArticleDOI
Improvement in radiologists' detection of clustered microcalcifications on mammograms. The potential of computer-aided diagnosis.
Heang Ping Chan,Kunio Doi,Carl J. Vyborny,Robert A. Schmidt,Charles E. Metz,Kwok Leung Lam,T Ogura,Yuzheng Wu,Heber MacMahon +8 more
TL;DR: In this paper, the authors studied the performance of computer-aided diagnostic (CAD) methods for the detection of clustered microcalcifications on mammograms and found that the results of their receiver operating characteristic (ROC) study show that CAD, as implemented by their computer code in its present state of development, does significantly improve radiologists' accuracy in detecting clustered microcifications under conditions that simulate the rapid interpretation of screening mammograms.
Book
Mammographic Image Analysis
TL;DR: A representation of the 'interesting' (non-adipose) tissue in a breast is developed and put to work to propose a new quantitative measure to aid in diagnosing masses and explore the possibility of reducing by half the radiation dose required for a mammogram.
Journal ArticleDOI
Decreased breast cancer mortality through mammographic screening: results of clinical trials.
TL;DR: An analysis of data from five major screening studies indicates that annual screening of all women aged 40 and over by means of state-of-the-art mammography, with two views per breast and physical examination, could reduce breast cancer mortality by at least 40% and possibly as much as 50%.
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