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2 edition of Automated detection of mammographic asymmetry. found in the catalog.

Automated detection of mammographic asymmetry.

Peter Ian Miller

Automated detection of mammographic asymmetry.

by Peter Ian Miller

  • 370 Want to read
  • 2 Currently reading

Published by University of Manchester in Manchester .
Written in English


Edition Notes

Manchester thesis (Ph.D.), Faculty of Medicine.

ContributionsUniversity of Manchester. Faculty of Medicine.
The Physical Object
Pagination148p.
Number of Pages148
ID Numbers
Open LibraryOL16572946M

Mammography (also called mastography) is the process of using low-energy X-rays (usually around 30 kVp) to examine the human breast for diagnosis and screening. The goal of mammography is the early detection of breast cancer, typically through detection of characteristic masses or microcalcifications.. As with all X-rays, mammograms use doses of ionizing radiation to create : cine. The development of computer-aided detection CAD systems for the automated search for pathologies could be very useful for the improvement of physicians’ diagnosis. A typical example is the analysis of mammographic im-ages, which are widely recognized as the only imaging mo-dality for an early detection of breast neoplasia.1,2 Breast.

This study aims to develop and test a new computer‐aided detection (CAD) approach and scheme, assessing the likelihood of a subject harboring breast abnormalities. Methods The proposed scheme is based on the analysis of both local and global bilateral mammographic feature : Adam Kelder, Dror Lederman, Dror Lederman, Bin Zheng, Yaniv Zigel. in a test mammographic image [18]. Bilateral image subtrac-tion [19] which implements difference between the left and right breast to obtain the suspicious regions, was one of the earliest method employed for the detection of the masses in mammograms. Most of File Size: KB.

of the developing asymmetry. Tissue diagnosis was obtained by core needle biopsy performed with 9- or gauge vacuum-assisted needles (ATEC, Hologic) using stereotactic or MRI guid - ance or with a gauge automated biopsy needle (Tru-Cut, Bard) using ultrasound guidance. Sur-gical excisions were performed using standard wire-localized excision. a given mammographic projection (Fig. 1). But in some cases, real lesions may manifest as an asymmetry. 2. Global Asymmetry ("Asymmetric Breast Tissue" in Third Edition of BIRADS): involves a greater volume of breast tissue over a significant portion of the breast (at least a quadrant), relative to the corresponding region in the contralateral.


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Automated detection of mammographic asymmetry by Peter Ian Miller Download PDF EPUB FB2

Computerized Analysis of Mammographic Images for Detection and Characterization of Breast Cancer Article in Synthesis Lectures on Biomedical Engineering 10(1) June with 78 Reads. This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry.

First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation by: A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry Stylianos D.

Tzikopoulosa,∗, Michael E. Automated detection of mammographic asymmetry. book Mavroforakisb, Harris V. Georgioua, Nikos Dimitropoulosc, Sergios Theodoridisa a National and Kapodistrian University of Athens, Dept.

of Informatics and Telecommunications, Panepistimiopolis,Cited by: A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry Article in Computer methods and programs in biomedicine (1) February.

We present a novel method to detect asymmetry in mammograms based upon bilateral analysis of the spatial distribution of density within paired mammographic strips. Various differential measures of spatial correlation of gray-scale values were computed with reference to the position of the nipple for a set of pairs of mammograms from the Cited by: 2.

Asymmetries are classified in four groups: 1. Asymmetry: as an area of fibroglandulair tissue visible on only one mammographic projection, mostly caused by superimposition of normal breast tissue Focal Asymmetry: visible on two projections, hence a real find in rather than superposition   A novel automated mammographic density measure and breast cancer risk.

J Natl Cancer Inst. ; – [PMC free article] Hubbard RA, Kerlikowske K, Flowers CI, et al. Cumulative probability of false-positive recall or biopsy recommendation after 10 years of screening mammography: A cohort study.

Ann Intern Med. ; –Cited by: (2) Asymmetry detection: asymmetry between the left and right breast images is detected using bilateral image subtraction. First, pairs of the thresholded left and right images are obtained at various intensity levels.

Second, the differences of each pair of images are detected using a Cited by: P. Miller and S. Astley. Automated detection of mammographic asymmetry using anatomical features. Int. of Pattern Recognition and Artificial Intelligence, –, CrossRef Google ScholarCited by: TOWARD AUTOMATED DETECTION AND DIAGNOSIS OF MAMMOGRAPHIC MICROCALCIFICATIONS ImadMohammadZyout, Ph.D.

WesternMichiganUniversity, Mammographie diagnosis is the most effective technique to detect breast cancer in its infancywhen it ismostresponsiveto treatment.

An earlyanda significant indicator. Mammographic density and the risk and detection of breast cancer. N Engl J Med ;(3)– Crossref, Medline, Google Scholar; 4.

Mandelson MT, Oestreicher N, Porter PL et al. Breast density as a predictor of mammographic detection: comparison of interval- and screen-detected cancers. J Natl Cancer Inst ;92(13)– Percent Density Analysis.

Percent mammographic density was measured by a fully automated research-based algorithm that uses “For Presentation” FFDMs to calculate an area-based measure of density as a percentage on a continuous scale (Figure 1, Panels 1(a) through 1(d)).Using view position and image laterality information from the DICOM header (elements (, ) and (, Cited by: 3.

Mammographie diagnosis is the most effective technique to detect breast cancer in its infancy when it is most responsive to treatment. An early and a significant indicator of breast cancer is the presence of clustered microcalcifications (MCs).

Mammographie MCs greatly vary in their appearance and shape, and become indistinguishable when surrounded by dense breast by: 4. Detection and Classification of Mammographic Calcifications (L Shen et al.) Comparative Evaluation of Pattern Recognition Techniques for Detection of Microcalcifications in Mammography (K S Woods et al.) Automated Detection of Breast Asymmetry Using Anatomical Features (P Miller & S Astley).

Institute of Physics and Engineering in Medicine. IPEM's aim is to promote the advancement of physics and engineering applied to medicine and biology for the public benefit. Its m. the mammographic image for the extraction of new statistical features, while the classi cation task is performed using Support Vector Machines (SVMs).

Finally, a new algorithm is proposed for breast asymmetry detection, using the feature values already extracted from the breast parenchymal density estimation step, using an one-class SVM classi Size: KB.

@article{osti_, title = {Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors: Automated measurement development for full field digital mammography}, author = {Fowler, E. and Sellers, T.

and Lu, B. and Heine, J. J.}, abstractNote = {Purpose: The Breast Imaging Reporting and Data System (BI-RADS) breast composition descriptors are used for. Computer‐aided detection of mammographic microcalcifications: Pattern recognition with an artificial neural network.

We are developing a computer program for automated detection of clustered microcalcifications on mammograms. the detection accuracy improved from a TP rate of 87% at an FP rate of four clusters per image to a TP rate of Cited by: AUTOMATIC DETECTION OF REGION OF INTERESTS IN MAMMOGRAPHIC IMAGES Erkang Cheng1 Haibin Ling1 Predrag R.

Bakic3 Andrew D.A. Maidment3 Vasileios Megalooikonomou2 1 Center for Information Science and Technology, Temple University 2 Data Engineering Laboratory (DenLab), Temple University 3 Department of Radiology, University of Pennsylvania ABSTRACT This work is a Cited by: 1.

The book is divided into four parts. In Part I, the anatomic, histopathologic, and mammographic views of the breast are examined, and the physics for different breast-imaging modalities are presented. Part II presents techniques for lesion and mass detection, and computer-aided diagnosis.

Computer-aided detection of bilateral asymmetry resulted in accuracy up towith sensitivity and specificity of 1 andrespectively. Computer-aided diagnosis of automatically detected lesions provided sensitivity of detection of malignant tumors in the range of [, ] at a range of falsely detected tumors of [, ] per : PaolaCasti, AriannaMencattini, MarcelloSalmeri, Rangaraj yan.aided diagnosis system for mammographic masses effectively.

Index Terms— Breast cancer, computer-aided diagnosis, mam-mography, mass detection, template matching. I. INTRODUCTION RECENTLY, in Japan, incidences of breast cancer have been increasing rapidly. It was reported that breast cancer has the highest mortality rate of any women’s cancer.Visual assessments of mammographic breast density by radiologists are used in clinical practice; however, these assessments have shown weaker associations with breast cancer risk than area-based, quantitative methods.

The purpose of this study is to present a statistical evaluation of a fully automated, area-based mammographic density measurement by: 3.