Literature DB >> 9368104

A simple method for automatically locating the nipple on mammograms.

R Chandrasekhar1, Y Attikiouzel.   

Abstract

This paper outlines a simple, fast, and accurate method for automatically locating the nipple on digitized mammograms that have been segmented to reveal the skin-air interface. If the average gradient of the intensity is computed in the direction normal to the interface and directed inside the breast, it is found that there is a sudden and distinct change in this parameter close to the nipple. A nipple in profile is located between two successive maxima of this parameter; otherwise, it is near the global maximum. Specifically, the nipple is located midway between a successive maximum and minimum of the derivative of the average intensity gradient; these being local turning points for a nipple in profile and global otherwise. The method has been tested on 24 images, including both oblique and cranio-caudal views, from two digital mammogram databases. For 23 of the images (96%), the rms error was less than 1 mm at image resolutions of 400 microns and 420 microns per pixel. Because of its simplicity, and because it is based both on the observed behavior of mammographic tissue intensities and on geometry, this method has the potential to become a generic method for locating the nipple on mammograms.

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Mesh:

Year:  1997        PMID: 9368104     DOI: 10.1109/42.640738

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  11 in total

1.  Model-based technique for the measurement of skin thickness in mammography.

Authors:  A Katartzis; H Sahli; J Cornelis; S Fotopoulos; G Panayiotakis
Journal:  Med Biol Eng Comput       Date:  2002-03       Impact factor: 2.602

2.  A software-based x-ray scatter correction method for breast tomosynthesis.

Authors:  Steve Si Jia Feng; Ioannis Sechopoulos
Journal:  Med Phys       Date:  2011-12       Impact factor: 4.071

3.  Identification of the breast boundary in mammograms using active contour models.

Authors:  R J Ferrari; R M Rangayyan; J E L Desautels; R A Borges; A F Frère
Journal:  Med Biol Eng Comput       Date:  2004-03       Impact factor: 2.602

4.  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

5.  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

6.  Radon-domain detection of the nipple and the pectoral muscle in mammograms.

Authors:  S K Kinoshita; P M Azevedo-Marques; R R Pereira; J A H Rodrigues; R M Rangayyan
Journal:  J Digit Imaging       Date:  2007-04-11       Impact factor: 4.056

7.  Objective models of compressed breast shapes undergoing mammography.

Authors:  Steve Si Jia Feng; Bhavika Patel; Ioannis Sechopoulos
Journal:  Med Phys       Date:  2013-03       Impact factor: 4.071

8.  Automatic detection of the nipple in screen-film and full-field digital mammograms using a novel Hessian-based method.

Authors:  Paola Casti; Arianna Mencattini; Marcello Salmeri; Antonietta Ancona; Fabio Felice Mangieri; Maria Luisa Pepe; Rangaraj Mandayam Rangayyan
Journal:  J Digit Imaging       Date:  2013-10       Impact factor: 4.056

9.  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 10.  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

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