Literature DB >> 8800610

Automatic detection of breast border and nipple in digital mammograms.

A J Méndez1, P G Tahoces, M J Lado, M Souto, J L Correa, J J Vidal.   

Abstract

Advances in the area of computerized image analysis applied to mammography may have very important practical applications in automatically detecting asymmetries (masses, architectural distortions, etc.) between the two breasts. We have developed a fully automatic technique to detect the breast border and the nipple, this being a necessary prerequisite for further image analysis. To detect the breast border, an algorithm that computes the gradient of gray levels was applied. To detect the nipple, three algorithms were compared (maximum height of the breast border, maximum gradient, and maximum second derivative of the gray levels across the median-top section of the breast). A combined method was also designed. The algorithms were tested on 156 digitized mammograms. The breast segmentation results were evaluated by two expert radiologists and one physicist. In 89% of the mammograms, the computed border was in close agreement with the radiologist's estimated border. Segmentation results were acceptable to be used in computer-aided diagnostic schemes. The mean distance between the position of the nipple indicated by two radiologists by consensus and the position calculated by the computer was 6 mm.

Mesh:

Year:  1996        PMID: 8800610     DOI: 10.1016/0169-2607(96)01724-5

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  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.  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

3.  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

4.  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

5.  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

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

7.  Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Authors:  Shubhi Sharma; Pritee Khanna
Journal:  J Digit Imaging       Date:  2014-07-09       Impact factor: 4.056

8.  Identification and segmentation of obscure pectoral muscle in mediolateral oblique mammograms.

Authors:  Chia-Hung Wei; Chih-Ying Gwo; Pai Jung Huang
Journal:  Br J Radiol       Date:  2016-04-04       Impact factor: 3.039

9.  A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Authors:  Manasi Hazarika; Lipi B Mahanta
Journal:  Asian Pac J Cancer Prev       Date:  2018-08-24

10.  Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer.

Authors:  Yassir Edrees Almalki; Toufique Ahmed Soomro; Muhammad Irfan; Sharifa Khalid Alduraibi; Ahmed Ali
Journal:  Sensors (Basel)       Date:  2022-02-26       Impact factor: 3.576

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