Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images

Yaniv Gal; Andrew Mehnert; Andrew Bradley; Kerry McMahon; Stuart Crozier

doi:10.1109/DICTA.2007.4426786

Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images

Yaniv Gal, Andrew Mehnert, Andrew Bradley, Kerry McMahon, Stuart Crozier

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

3 Citations (Scopus)

Abstract

We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.

Original language	English
Title of host publication	Proceedings - Digital Image Computing Techniques and Applications
Subtitle of host publication	9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007
Pages	124-129
Number of pages	6
DOIs	https://doi.org/10.1109/DICTA.2007.4426786
Publication status	Published - 1 Dec 2007
Externally published	Yes
Event	Australian Pattern Recognition Society (APRS) - Glenelg, SA, Australia Duration: 3 Dec 2007 → 5 Dec 2007

Conference

Conference	Australian Pattern Recognition Society (APRS)
Country/Territory	Australia
City	Glenelg, SA
Period	3/12/07 → 5/12/07

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10.1109/DICTA.2007.4426786

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Gal, Y., Mehnert, A., Bradley, A., McMahon, K., & Crozier, S. (2007). Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images. In Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007 (pp. 124-129). Article 4426786 https://doi.org/10.1109/DICTA.2007.4426786

@inproceedings{b430e6b5ebf4444ca6c3cbd041c9e219,

title = "Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images",

abstract = "We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.",

author = "Yaniv Gal and Andrew Mehnert and Andrew Bradley and Kerry McMahon and Stuart Crozier",

year = "2007",

month = dec,

day = "1",

doi = "10.1109/DICTA.2007.4426786",

language = "English",

isbn = "0769530672",

pages = "124--129",

booktitle = "Proceedings - Digital Image Computing Techniques and Applications",

note = "Australian Pattern Recognition Society (APRS) ; Conference date: 03-12-2007 Through 05-12-2007",

}

Gal, Y, Mehnert, A, Bradley, A, McMahon, K & Crozier, S 2007, Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images. in Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007., 4426786, pp. 124-129, Australian Pattern Recognition Society (APRS), Glenelg, SA, Australia, 3/12/07. https://doi.org/10.1109/DICTA.2007.4426786

Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images. / Gal, Yaniv; Mehnert, Andrew; Bradley, Andrew et al.
Proceedings - Digital Image Computing Techniques and Applications: 9th Biennial Conference of the Australian Pattern Recognition Society, DICTA 2007. 2007. p. 124-129 4426786.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images

AU - Gal, Yaniv

AU - Mehnert, Andrew

AU - Bradley, Andrew

AU - McMahon, Kerry

AU - Crozier, Stuart

PY - 2007/12/1

Y1 - 2007/12/1

N2 - We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.

AB - We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.

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M3 - Conference paper

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SN - 0769530672

SN - 9780769530673

SP - 124

EP - 129

BT - Proceedings - Digital Image Computing Techniques and Applications

T2 - Australian Pattern Recognition Society (APRS)

Y2 - 3 December 2007 through 5 December 2007

ER -

Automatic segmentation of enhancing breast tissue in dynamic contrast-enhanced MR images

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