Literature DB >> 8208220

Computerized detection of masses in digital mammograms: automated alignment of breast images and its effect on bilateral-subtraction technique.

F F Yin1, M L Giger, K Doi, C J Vyborny, R A Schmidt.   

Abstract

An automated technique for the alignment of right and left breast images has been developed for use in the computerized analysis of bilateral breast images. In this technique, the breast region is first identified in each digital mammogram by use of histogram analysis and morphological filtering operations. The anterior portions of the tracked breast border and computer-identified nipple positions are selected as landmarks for use in image registration. The paired right and left breast images, either from mediolateral oblique or craniocaudal views, are then registered relative to each other by use of a least-squares matching method. This automated alignment technique has been applied to our computerized detection scheme that employs a nonlinear bilateral-subtraction method for the initial identification of possible masses. The effectiveness of using bilateral subtraction in identifying asymmetries between corresponding right and left breast images is examined by comparing detection performances obtained with various computer-simulated misalignments of 40 pairs of clinical mammograms. Based on free-response receiver operating characteristic and regression analyses, the detection performance obtained with the automated alignment technique was found to be higher than that obtained with simulated misalignments. Detection performance decreased gradually as the amount of simulated misalignment increased. These results indicate that automatic alignment of breast images is possible and that mass-detection performance appears to improve with the inclusion of asymmetric anatomic information but is not sensitive to slight misalignment.

Mesh:

Year:  1994        PMID: 8208220     DOI: 10.1118/1.597307

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  10 in total

1.  Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling.

Authors:  R J Ferrari; R M Rangayyan; R A Borges; A F Frère
Journal:  Med Biol Eng Comput       Date:  2004-05       Impact factor: 2.602

2.  Computerized nipple identification for multiple image analysis in computer-aided diagnosis.

Authors:  Chuan Zhou; Heang-Ping Chan; Chintana Paramagul; Marilyn A Roubidoux; Berkman Sahiner; Labomir M Hadjiiski; Nicholas Petrick
Journal:  Med Phys       Date:  2004-10       Impact factor: 4.071

Review 3.  Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.

Authors:  Maryellen L Giger; Heang-Ping Chan; John Boone
Journal:  Med Phys       Date:  2008-12       Impact factor: 4.071

4.  Optical Fourier techniques for medical image processing and phase contrast imaging.

Authors:  Chandra S Yelleswarapu; Sri-Rajasekhar Kothapalli; D V G L N Rao
Journal:  Opt Commun       Date:  2008-04-01       Impact factor: 2.310

5.  Computer-aided detection scheme for sentinel lymph nodes in lymphoscintigrams using symmetrical property around mapped injection point.

Authors:  Ryohei Nakayama; Akiyoshi Hizukuri; Koji Yamamoto; Nobuo Nakako; Naoki Nagasawa; Kan Takeda
Journal:  J Digit Imaging       Date:  2012-02       Impact factor: 4.056

6.  A heuristic approach to automated nipple detection in digital mammograms.

Authors:  Mainak Jas; Sudipta Mukhopadhyay; Jayasree Chakraborty; Anup Sadhu; Niranjan Khandelwal
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

Review 7.  Breast image registration techniques: a survey.

Authors:  Yujun Guo; Radhika Sivaramakrishna; Cheng-Chang Lu; Jasjit S Suri; Swamy Laxminarayan
Journal:  Med Biol Eng Comput       Date:  2006-03       Impact factor: 2.602

8.  Image analysis in medical imaging: recent advances in selected examples.

Authors:  G Dougherty
Journal:  Biomed Imaging Interv J       Date:  2010-07-01

Review 9.  Cancer Diagnosis Using Deep Learning: A Bibliographic Review.

Authors:  Khushboo Munir; Hassan Elahi; Afsheen Ayub; Fabrizio Frezza; Antonello Rizzi
Journal:  Cancers (Basel)       Date:  2019-08-23       Impact factor: 6.639

10.  Bilateral Image Subtraction and Multivariate Models for the Automated Triaging of Screening Mammograms.

Authors:  José Celaya-Padilla; Antonio Martinez-Torteya; Juan Rodriguez-Rojas; Jorge Galvan-Tejada; Victor Treviño; José Tamez-Peña
Journal:  Biomed Res Int       Date:  2015-07-09       Impact factor: 3.411

  10 in total

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